Shares of Synopsys Inc. inched 0.50% higher to $594.32 Monday, on what proved to be an all-around rough trading session for the stock market, with the S&amp;P...

Synopsys Inc. stock outperforms competitors on strong trading day - MarketWatch

Shares of Synopsys Inc. shed 4.89% to $565.26 Tuesday, on what proved to be an all-around dismal trading session for the stock market, with the S&amp;P 500 Index...

Synopsys Inc. stock underperforms Tuesday when compared to competitors - MarketWatch

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Synopsys, Inc. provides electronic design automation software products used to design and test integrated circuits. The company offers Digital and Custom IC Design solution that provides digital design implementation solutions; Verification solution that offers virtual prototyping, static and formal verification, simulation, emulation, field programmable gate array (FPGA)-based prototyping, and debug solutions; and FPGA design products that are programmed to perform specific functions. It also provides intellectual property (IP) solutions for USB, PCI Express, DDR, Ethernet, MIPI, HDMI, and Bluetooth low energy applications; logic libraries and embedded memories; processor cores, software, and application-specific instruction-set processor tools for embedded applications; security IP solutions; IP solutions for automotive market; and system-on-chip (SoC) infrastructure IP, datapath and building block IP, and verification IP products, as well as mathematical and floating-point components, and Arm AMBA interconnect fabric and peripherals. In addition, the company offers HAPS FPGA-based prototyping systems; virtual prototyping solutions; and Platform Architect solutions for SoC architecture analysis and optimization, as well as a series of tools used in the design of optical systems and photonic devices. Further, it provides security testing, managed services, programs and professional services, and training that enable its customers to detect and remediate security vulnerabilities, and defects in the software development lifecycle, as well as manufacturing solutions. The company serves electronics, financial services, automotive, medicine, energy, and industrial areas. Synopsys, Inc. was incorporated in 1986 and is headquartered in Mountain View, California.

SYNOPSYS INC

As the world becomes increasingly digital, nearly every company is looking for to code—no matter the industry. The phenomenon has been great news for software engineers and programmers who can now command high salaries for their hot, in-demand talent. software engineers Perhaps it is no surprise that software engineers in the San Francisco Bay Area, home to Silicon Valley and its many tech giants, can command an even higher premium with their pay packages. According to the more than 5 million verified professionals on the professional social network Blind, these are the top 17 best-paying cities for software engineers in the San Francisco Bay Area. Blind considered the annual base pay and the total compensation package, including the annual salary and any stock-based compensation or bonuses a company might offer. 1. Los Gatos, California n: $500,449 Average Total Compensatio : $475,000 Median Total Compensation : $470,015 Average Salary : $451,000 Median Salary Los Gatos is the best-paying city for software engineers in the U.S., according to Blind. The small town is home to Netflix, which prefers to hire more senior or experienced professionals and pays accordingly. The video-streaming giant typically pays large salaries and gives few employees stock-based compensation. 2. Menlo Park, California : $328,423 Average Total Compensation : $297,000 Median Total Compensation : $167,004 Average Salary : $165,000 Median Salary Menlo Park might be home to only 34,000 people, but its population can swell in size with tech workers during the workweek. Menlo Park is home to the headquarters of Meta, the owner of Facebook and one of the highest-paying technology companies. Google and Roblox were also founded in the city. 3. Cupertino, California n: $279,391 Average Total Compensatio : $255,850 Median Total Compensation : $165,753 Average Salary : $165,000 Median Salary Cupertino is home to Apple and its futuristic Apple Park corporate campus. Software engineers at Apple have enjoyed large paychecks recently, as the iPhone maker briefly topped a jaw-dropping $3 trillion market cap early in 2022. 4. San Bruno, California : $278,368 Average Total Compensation : $225,900 Median Total Compensation : $161,269 Average Salary : $149,500 Median Salary YouTube moved its headquarters to San Bruno in 2007 and has become the city’s largest employer. The Google-owned video streaming service employs more than 2,000 professionals. 5. Mountain View, California : $275,181 Average Total Compensation : $242,000 Median Total Compensation : $160,790 Average Salary : $156,000 Median Salary Mountain View is synonymous with Silicon Valley. Long before startups were birthed in San Francisco Bay Area garages, the city was home to silicon semiconductor producers, which gave the region its name. Of course, the city is now well-known as the headquarters of Google, but other tech companies with prominent presences include 23andMe, Intuit, Mozilla and Synopsys. 6. Oakland, California : $266,869 Average Total Compensation : $197,750 Median Total Compensation : $192,240 Average Salary : $168,750 Median Salary Oakland has become a hub for many financial-technology companies. Credit Karma moved its headquarters from San Francisco to downtown Oakland in 2020. Block and Marqeta also have major offices in “The Town.” 7. San Francisco, California : $265,050 Average Total Compensation : $225,000 Median Total Compensation : $165,042 Average Salary : $160,200 Median Salary San Francisco might be the crown jewel of the technology industry, as the home of too many tech startups and companies to count. The city by the bay is home to Airbnb, DoorDash, Lyft, Twitter, Uber and more, but its top employer is . The cloud giant employs more than 9,000 employees, and its headquarters has made a permanent contribution to the downtown skyline. Salesforce 8. Sunnyvale, California : $260,048 Average Total Compensation : $238,000 Median Total Compensation : $160,499 Average Salary : $160,000 Median Salary Sunnyvale maintains its Silicon Valley roots as home to the chip-maker AMD and advanced technology and networking companies, including Juniper Networks, Microsoft-owned LinkedIn and NetApp. 9. Palo Alto, California : $234,739 Average Total Compensation : $200,000 Median Total Compensation : $157,488 Average Salary : $154,000 Median Salary The tony city of Palo Alto is one of the major cities of Silicon Valley. Cloudera and VMware are some of the largest technology companies based in the city. 10. San Mateo, California : $234,066 Average Total Compensation : $187,875 Median Total Compensation : $158,629 Average Salary : $155,500 Median Salary Companies that call San Mateo home include Coupa, Roblox and Snowflake. Before being acquired by Zendesk, Momentive, which was founded as SurveyMonkey, was headquartered in the city. 11. Pleasanton, California : $225,058 Average Total Compensation : $202,000 Median Total Compensation : $150,887 Average Salary : $150,000 Median Salary Pleasanton is home to many business parks, but the suburb’s top employer is the HR-technology giant Workday. 12. Foster City, California : $220,293 Average Total Compensation : $190,000 Median Total Compensation : $158,319 Average Salary : $148,500 Median Salary Foster City is the headquarters site of the biopharmaceutical giant Gilead Sciences and the enterprise software company Zuora, among other companies. 13. Fremont, California : $218,913 Average Total Compensation : $180,000 Median Total Compensation : $144,638 Average Salary : $142,500 Median Salary Notable companies with headquarters in Fremont include Corsair, Synnex and more, although the city’s No. 1 employer is Tesla. 14. Santa Clara, California : $214,168 Average Total Compensation : $197,000 Median Total Compensation : $153,885 Average Salary : $150,000 Median Salary AMD, Intel, NVIDIA and Palo Alto Networks are some of the most well-known tech companies headquartered in Santa Clara. 15. San Jose, California n: $210,801 Average Total Compensatio : $183,000 Median Total Compensation : $153,390 Average Salary : $145,000 Median Salary The largest city by population in the San Francisco Bay Area, San Jose is home to Adobe, Cisco, eBay, PayPal, Zoom and more. 16. Redwood City, California : $207,955 Average Total Compensation : $190,000 Median Total Compensation : $156,248 Average Salary : $151,000 Median Salary Redwood City is the headquarters location for several global technology companies, such as Box, Carbon, Electronic Arts and Informatica. 17. Milpitas, California : $188,146 Average Total Compensation : $165,750 Median Total Compensation : $154,073 Average Salary : $138,000 Median Salary Some of the top employers in Milpitas are tech employers, including Cisco, FireEye, SanDisk and more. Methodology Blind analyzed the base salaries and total compensation of software engineers in the San Francisco Bay Area on its platform. Cities were ranked by the average total compensation, including annual base salary and, if applicable, stock-based compensation and cash bonuses. All data is self-reported by verified professionals on the professional social network Blind. Also published on Teamblind's blog

17 Highest-Paying Cities in the San Francisco Bay Area for Software Engineers

Las 17 ciudades que mejor pagan en el área de la bahía de San Francisco para ingenieros de software

17 villes les mieux rémunérées de la région de la baie de San Francisco pour les ingénieurs logiciels

सॉफ्टवेयर इंजीनियरों के लिए सैन फ्रांसिस्को खाड़ी क्षेत्र में 17 उच्चतम-भुगतान वाले शहर

ソフトウェア エンジニアにとって、サンフランシスコ ベイエリアで最も給料の高い 17 の都市

17 cidades mais bem pagas na área da baía de São Francisco para engenheiros de software

17 thành phố trả lương cao nhất trong khu vực vịnh San Francisco cho các kỹ sư phần mềm

旧金山湾区软件工程师薪酬最高的 17 个城市

Photo by on Jad Limcaco Unsplash Modern software development involves the increased use of open-source code as an essential part of tailoring product projects. The Linux Foundation survey on corporate open source programs says that 72% of organizations make non-commercial use of open source code and 55% use it for commercial product development. related to the use of open-source code that you and your team might wish to avoid in your product development project. Read on and learn how to prevent future or fix current flaws. We have outlined three key bottlenecks Problem 1: Violation of the License Agreement for Commercial Uses Using unlicensed open-source сode is unsafe. You might end up violating intellectual property rights or bringing security vulnerabilities and risks into your project, which can translate into financial and legal consequences. Despite that, most developers strive to release faster or cut costs so much that they don’t stop to think about the origin of the components they are going to use. Now, how does licensing work? A code license allows developers to use, copy, modify, and share the component in a certain way, while unlicensed parts make those aspects unclear. There are 20 common licenses, such as Apache License 2.0 and others. Still, 33% of codebases contain unlicensed open source components. So if you bump into an unlicensed component, be extremely careful. Adopting unlicensed components entails great risks of copyright violations. Tip: Watch out for Hidden Inconsistencies. You must be wondering how to stay on the safe side. document the use of all third-party resources on the project. Although it requires time and resources, you get to know where all your open-source elements come from. Firstly, import libraries only after getting approval from the project tech lead. Secondly, Problem 2: Use of libraries without community support open-source code in product development Another aspect you might stumble over is using open source libraries that a developer community doesn’t support. These libraries might often fail to comply with security standards, work incorrectly with other open-source components, be out-of-date, or have no license at all. Furthermore, developers of open source components may create custom licenses or distribute their code without any information. Black Duck Audits says that out of all audited codebases, 31% of open source components have custom licenses, and their usage might result in license conflict. Business and legal risks are likely aftermaths that your development team may wish to dodge by comprehensively analyzing open-source code and its origin. Open source code that is supported by the community usually comes with regular updates as its creators take responsibility to ensure security compliance and compatibility with new language versions. They focus on constant and timely delivery of all necessary support, thus reducing the risk of product developers using outdated code. Moreover, the community is usually quick to detect bugs and fix them. All this dramatically increases the trustability of open source libraries that enjoy community support. Tip: Check the origin of the libraries you use. If your project requires an open-source library, start with scrutinizing the component you need: сheck its license, source, and version before you use it. Otherwise, your engineering team may neglect the license policy, which is likely to ruin the final product and result in legal action from copyright holders. Also, try to only use libraries from official sites, and if possible, do not import code manually. Do it with the help of builders instead. There is another way that might work for you: opt for the help of a professional provider of software evaluation services. You’ll have a highly qualified team at your service, sharing their vast experience of product quality assessment, and doing research instead of your engineers. Problem 3: Use of outdated open-source components causing vulnerability risks open-source code in product development A huge number of product development projects (91%, to be precise) use open source components, thus jeopardizing project security significantly. outdated According to Synopsys, 82% of codebases have four-year-old parts and 88% have had no add-ons during the last two years. Providers of open source code sooner or later withdraw support of earlier versions and stop delivering new patches. If your organization is using legacy software, this may result in security breaches. Your business-critical files or customer data may be stolen or become publicly available as it happened to the American credit ranking giant Equifax. They used an open-source server framework that had security vulnerabilities already detected by the solution provider. The latter had released the patch to fix the issue, but the credit ranking company continued using the outdated version. This company suffered a hacker attack through the vulnerable open source code, and 143 million sensitive customer records leaked into the public domain. Tip: Track the software versions you use. To avoid security risks, failures and corruption of data-related processes, you need to be sure that the software you use is brand new and receives vendor support and upgrades. In case you work with different frameworks, you need to check that all libraries work together correctly. Final recommendations: The following general steps can help you deal with major open-source code issues: Inventory your open-source components right now. Structured records can help you navigate through and monitor all the elements. Create policies for your development and legal teams to regulate every open source activity in the project. Keep on auditing your open-source code regularly. It is the best way to detect and troubleshoot issues on time. Engage in open source communities. In this way, your engineers improve their knowledge of the components they employ in the project. If you are not confident about the product quality and wish to scrutinize your open source components, go for a large-scale software project assessment. We recommend the TETRA platform. It can help you uncover technical debt and get an in-depth analysis of code quality, as well as useful ideas for solving your burning issues. Also published here.

How to Include Open Source Code in Your Product 

Artificial intelligence is a technology that surprised the world by evolving from a fictional tale in movies to now having a ubiquitous influence in every sphere of human life. But was this an unpredictable change? Not really. The algorithms and experiments started back in the 1950s. It built the foundation of the AI we know today, like business automation software, the infamous Alexa and one of the biggest accomplishments of AI this year - . ChatGPT This level of evolution is possible only if the right minds put on their thinking caps and begin execution in the right direction. Lets See The List of 10 AI Development Companies in USA 1. Google Cloud Google Cloud offers an array of machine learning and artificial intelligence tools for developers and data scientists. These tools can be used at every stage of the development pipeline, i.e. preparation, building, validation and deployment of AI platforms. Google Cloud’s services primarily include virtual resources and physical assets. Some tools available on the platform include , Vertex AI Workbench, Cloud Natural Language, Video AI, Vision AI, and AutoMl, to name a few. Its services extend to infrastructure development tools for building prototypes, scaling, and deploying projects. Vertex AI For instance, its Deep Learning Containers have pre-configured deep learning environments which can assist in building prototypes. And the high-performing GPUs help with scientific computing and ease specific workloads. For its remarkable vision and resources, in 2022, in Magic Quadrant for Cloud AI Developer Services. Some noteworthy clients of Google Cloud include PayPal, Gojek, 20th Century Fox, P&G and Twitter. Gartner named Google Cloud the leader Launched: April 7, 2008 Employees: 32000+ Stock Price: USD 96.94 (as of 16th February 2023) 2.  Inc. RisingMax Inc. RisingMax is one of the leading AI company, acts as a digital transformation catalyst for startups and FORTUNE-500 companies. They are obliged to assist businesses worldwide in reducing their carbon footprint and creating a sustainable future. With the strong eco-community, they support green tech initiatives and reward businesses working to build a better tomorrow. They celebrate the project's success by planting 100+ trees each time. By rewarding or compensating the plating cost, they also encourage our clients to plant at least 50 trees. 3. OpenAI Open AI is a non-profit AI research and deployment company based in San Francisco, California. Open AI’s hall of fame includes Chat GPT, DALL·E 2 and Whisper. Chat GPT is a masterpiece of conversational AI with the ability to produce unique dialogues based on inputs. DALL·E 2 can create original and realistic image compositions based on descriptions. Whisper is an automatic speech recognition tool that can go through any quality of audio in any language and transcribe it accurately. Additionally, Open AI has other development tools to offer, like GPT3 and Codex. Users can also leverage Open AI’s free content filtering, end-user monitoring and scoping of API usage services. Pacific Northwest National Laboratory, Scale Media Inc., and Stellar Science Ltd. currently use OpenAI services. As per , OpenAI aims to achieve revenue of $1 billion by 2024. With investors like Microsoft, Khosla Ventures and Reid Hoffman’s charitable foundation, the future of  AI innovation in OpenAI looks bright. Reuters Launched: December 10, 2015 Employees: 375+ Stock Price: Open AI is a private company as of February 16, 2023 4. IBM Cloud IBM is leading the AI sector with its AI technologies. IBM cloud features a list of tools, including AI and Machine learning tools, specifically . It was released in April 2010 and is still actively used to build machine learning models. Watson AI IBM Watson Assistant, Discovery, Knowledge Catalog, Language Translator, Knowledge Studio and Natural Language Classifier are some useful AI tools available on IBM Cloud. In 2020 AI market share report with the largest market share of 13.7%. IBM received the #1 ranking in the IDC IBM Cloud’s Watson AI is used by 70% of banking institutions and by some of the top companies like Panasonic, Covéa, DB Dialog & DB Systel and Humana Inc. Its use cases in multiple industries spread across 21 verticals, including insurance, aerospace, defense, banking and transportation. Launched: April 2010 Employees: 300k+ Stock Price: USD 136.4 (as of 16th February 2023) 4. Amazon Web Services (AWS) Amazon Web Services has a comprehensive set of AI and machine learning tools targeting business analysts, data scientists, and engineers, allowing them to build, train, and deploy their ML models even without ML experience. Users can engage with ML experts and utilize AWS’s consulting services to complete their projects. The platform has a list of free tier tools like Amazon Polly, , Amazon Comprehend, Amazon Translate, Amazon Lex and Amazon Transcribe. AWS boasts a partnership with industry leaders like Intel, Accenture, Deloitte and C3 AI. Amazon SageMaker AWS AI has been employed by many startups and enterprises, such as Amazon, Discovery, Intuit, Formula 1 and Cambia Health Solutions. For its successful operation, Gartner recognized it as a leader in the Cloud AI Developer services' Magic Quadrant. Launched: November 2016 Employees: 40,000+ Stock Price: USD 101.16 (as of 16th February 2023) 6. Alibaba Cloud Alibaba Cloud is one of the leaders in the cloud computing and AI industry, with tools and services for developers and enterprises. With its end-to-end solutions, such as data processing, model training, prediction and evaluation, it aims to make AI more accessible to all. Alibaba Cloud’s AI tools include technologies like Vision AI, Intelligent Speech, NLP, Interactive AI and Intelligent Marketing. Its services are spread across many verticals like finance, education, transportation and new retail. The recognized Alibaba Cloud as one of the top 3 public cloud enterprise container platforms. Companies like Newegg, Blackboard, PicsArt and Align Technology leverage the AI services of Alibaba Cloud. Forrester report Launched: September 2009 Employees: 3000+ Stock Price: USD 103.08 (As of February 16, 2023) 7. Microsoft Azure Microsoft Azure AI services create a new way to access breakthrough research in the industry. The platform’s easy API calls can help data scientists and data engineers access language, vision, speech and decision-making AI. Broadly, the Azure services include Azure Applied AI, Azure Machine Learning, Azure Infrastructure and Azure Cognitive Services, each of which can enhance business processes, accelerate performance and add an extra layer of security. Specifically, Azure offers machine learning tools like Jupyter, Notebooks and Visual Studio Code and open-source frameworks like TensorFlow and PyTorch. Besides its low-code and no-code tools, services give users access to powerful AI tools like GitHub Copilot and GPT3. Azure’s Open AI Microsoft Azure was recognized as a leader in the ‘2021 IDC MarketScape for worldwide general-purpose conversational AI platforms.’ GigaOm also positioned . Azure’s active clients include industry leaders like BBC, NHS, Twitter and NBA. Azure among the leaders in the MLOps Radar Launched: February 2010 Employees: 200,000+ Stock Price: USD 269.32(As of February 16, 2023) 8. Salesforce Headquartered in San Francisco, California, Salesforce presents a large infrastructure of AI tools for admins and developers to build, train and deploy AI projects. Einstein is the most well-known platform in Salesforce, with no-code pre-built solutions and custom AI features. It’s well known for laying a rich set of AI tools for smart apps, such as Einstein Prediction Builder, Einstein Bots, Einstein Vision, Einstein Language and Einstein Language Discovery. Salesforce has been recognized as a leader, for the 16th consecutive year, in the 2022 Quadrant™ for Sales Force Automation Platforms. Some of the well-known clients of Salesforce include notable names like Ford, Loreal, Hologic and Engie. Gartner® Magic Launched: 2016 Employees: 73000+ Stock Price: USD 171.10 (As of February 16, 2023) 9. Deepmind Started in 2010, Deepmind is a research laboratory with a team of scientists, engineers and ethicists innovating AI. It’s headquartered in London with offices in other parts of the world like Canada, France and the United States. , The company achieved success early on when its AlphaGo program became the first to beat the professional Go Player. After partnering with Google in 2014, Deepmind was able to develop programs that detect eye diseases. According to BBC Its innovative AI is used to personalize recommendations in Google Play and predict patient deterioration in the U.S. Department of Veterans Affairs. Launched: 2010 Employees: 1000+ Stock Price: USD 96.94 (As of February 16, 2023) 10. Samsung Samsung is bringing forth its research in AI to build innovative solutions and smart devices. Samsung’s AI is currently focused on AI Fundamentals, Language and Voice AI and Vision AI. The company has expanded to five countries and established 7 AI centers in Korea, the UK, Canada, Russia and the USA. At , the company unveiled its vision of innovating technology with AI-based semiconductors. Samsung became one of the first to create AI chips using Synopsys AI software. These AI chips, called the , are used in most of the Samsung smartphones built today, like the Galaxy S20 and the Note 10 Lite. Samsung AI Forum 2022 Exynos Today, the company’s list of smart devices also includes the AI EcoBubble washer, which analyzes the patterns and determines the best washing cycle for its users. Launched: March 1938 Employees: 266,000+ Stock Price: 63,700 KRW (As of February 16, 2023) 11. Neuralink Neuralink is a neurotechnology company based in the US and owned by Elon Musk. The company's purpose is to find the brain-computer connection and build a chip that can be implanted surgically in the skull. The chip will be called the N1 chip with an 8mm diameter and electrodes and insulation for the wires. In 2021, Neuralink made a showing a monkey with a brain chip playing a video game. According to , the chip may help bring back someone’s eyesight and fix any damage to the brain. video Musk Founded: 2016 Employees: 300+ Stock Price: USD 0.000008668 (As of February 16, 2023) Final Words: What Is the Future of AI? From AI software writing an entire novel to a chip that can control humans using an app, AI innovations are not easy to comprehend. But these companies have the resources, the skills and the determination to build something big in this direction. Our list of the Top AI development companies includes the leaders that are commanding the AI industry today. Soon, AI will take over every sphere of our lives and become an indispensable aspect of every business.

Top 10 AI Development Companies in USA

10 empresas de desarrollo de IA que lideran la innovación en IA en 2023

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10 एआई विकास कंपनियां 2023 में एआई इनोवेशन का नेतृत्व कर रही हैं

2023 年の AI イノベーションをリードする AI 開発企業 10 社

10 empresas de desenvolvimento de IA líderes em inovação de IA em 2023

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2023年引领人工智能创新的10家人工智能开发公司

Penetration Testing And Vulnerability Scanning

অনুপ্রবেশ পরীক্ষা এবং দুর্বলতা স্ক্যানিং

Penetrationstests und Schwachstellenscans

Pruebas de penetración y escaneo de vulnerabilidades

Tests d'intrusion et analyse des vulnérabilités

प्रवेश परीक्षण और भेद्यता स्कैनिंग

侵入テストと脆弱性スキャン

침투 테스트 및 취약점 검색

Teste de penetração e verificação de vulnerabilidades

Тестирование на проникновение и сканирование уязвимостей

Sızma Testi ve Güvenlik Açığı Taraması

Kiểm tra thâm nhập và quét lỗ hổng

渗透测试和漏洞扫描

Hi, HackerNoon! My name is Sofia, and I am a DevOps/Cloud engineer. I decided to participate in the contest by HackerNoon and Aptible. In this article, I will talk about the features of the DevSecOps pipeline and the concept of Shift Left. You will learn about the main stages of the DevSecOps pipeline, automated security checks in software development, and free and open-source tools. You'll also find tips to help you detect vulnerabilities earlier and improve application security. This article will help you assess the maturity of your DevSecOps pipeline, develop a roadmap for its development, choose the right tools for each task, and better understand how to manage projects following the DevSecOps philosophy. Shift Left Concept The main goal of the DevSecOps methodology is to introduce automated security checks into the DevOps pipelines, i.e., into the software development process itself. Not so long ago, information security specialists conducted tests after completing the development process. This approach is inefficient - if errors are discovered during security testing, the entire development cycle has to be restarted. This is time-consuming and expensive. Take a look at the image below. You can see that the cost of error correction increases with each step. It costs almost nothing to fix security issues discovered during development and functional testing. All necessary changes can be made to the source code immediately and sent for rechecking. Fixing problems at the artifact build stage is at least twice as expensive. You will have to make changes to the build process, for example, edit the Dockerfile, which means you will need the help of DevOps engineers. If an error is detected during integration testing, it will be 10 times more expensive to fix it. In this case, you must restart the development cycle and involve developers, DevOps engineers, and testers. Security problems detected at the deployment stage can lead to user data leaks. The company can receive millions of dollars in fines and damage to its reputation, which means the cost of a mistake increases hundreds of times. Hence, the main conclusion is that safety checks should be performed as early as possible. If you visualize it, move them to the left side of the Pipeline. This is the concept of Shift Left, which security experts love to talk about so much. DevSecOps Pipeline Structure DevSecOps pipelines are an automated chain of processes and tools that build, test, and deliver applications in a production environment and secure them at every stage. The image above shows the DevSecOps Pipeline structure with all the main phases of security checks. Here are a few words about each of them: Pre-commit. It implies checking the security of source code before the developer commits the code to the version control system. This check allows you to detect unencrypted confidential information such as passwords or API keys. Pre-build. It implies checking the security of source code after it has entered the version control system. The tools perform static analysis of source code and its dependencies to detect common vulnerabilities, such as many of the list. OWASP Top Ten Post-build. It is the security check of an artifact built from source code, such as a Docker file, RPM package, or JAR archive. The tools analyze the application environment and dependencies, finding outdated versions of packages and libraries that already have patches in newer versions. Test-time. It implies testing the security of an application running from a collected artifact. Tools in this phase try to "break" the application by simulating the attackers' actions. Post-deployment. It implies checking the security of an application after it has been deployed in a production environment. The tools monitor open registries of known vulnerabilities in real time and notify when a threat to the application is detected. Next, let's take a closer look at what these checks are and what tools are used to perform them. Pre-commit Checks Pre-commit checks allow you to analyze the source code on the developer's machine before committing changes to the local copy of the repository. If any of the statements returns an error, the commit is not performed. In this case, the developer must fix the mistake and commit again. This check avoids the situation when unchecked code, for example, with unencrypted secrets, gets into the repository. For performing pre-commit source code checks, it is necessary to install tools on developers' local machines. This approach has not only advantages but also disadvantages. For example, environmental differences: different versions of the instruments, various operating systems, and even processor architectures. If developers use different versions of pre-commit tools, the results of checking will differ, and this may make it challenging to work together. Consider this when implementing pre-commit checks within a team or across the company. Tools for Pre-Commit Checks Many open-source tools can be used to set up pre-commit checks: Gitleaks git-secrets Trivy Secret Scanning Whispers git-all-secrets detect-secrets gitty leaks These are great tools for pre-commit checks. Pre-Build Checks In the pre-build phase, White Box Testing is performed. These checks are used to analyze the source code, like in the previous step. In this case, the application is a "white box" because we know and understand its architecture and internal structure. There are several types of pre-build checks: Secret Detection Static Application Security Testing (SAST) Software Composition Analysis (SCA) Now, let’s discuss them in detail. Pre-build Secret Detection Check Secret Detection is an automated check that detects unencrypted sensitive information: unencrypted secrets in source code that have entered a version control system. Pre-build checks are performed after the source code has entered the repository of the version control system. Therefore, all unencrypted sensitive data in the storage can potentially be accessed by attackers, for example, if the repository's contents are leaked. In addition, the process of implementing secret-detection checks may occur not from scratch but in an evolving project. In this case, unencrypted secrets may be found in old commits and different repository branches. To solve these problems, we need to do many different things. For example, we must clean repositories of unencrypted secrets or implement various secret management tools like . Hashicorp Vault Tools for Secret Detection Secret Detection can be configured using , , or . However, it is best to use services provided by well-known CI/CD platforms, for example, . Gitleaks git-secrets detect-secrets GitLab Secret Detection Pre-Build SAST Check Static Application Security Testing (SAST) is a test when analyzers do not just check syntactic correctness but also measure code quality with the help of unique metrics. The main task of SAST scanners is security testing. In particular, SAST-analysers contain source code for common vulnerabilities, for example, some of the lists. It is essential to say that SAST scanners find not only the vulnerability itself but also the code fragment that caused it. OWASP Top Ten SAST analysis is called White Box Testing because the analyzer can access the application's internal structure. It is important to note that analyzers check the source code without running it, so they may generate false positives and fail to detect some vulnerabilities. For this reason, you should not limit yourself to SAST analysis only. It is better to approach the issue comprehensively and use different types of analysis: SCA, DAST, IAST, and OAST. SAST Tools Proprietary solutions: e SonarQub Checkmarx SAST Solar appScreener The free solution is GitLab SAST. Pre-Build Source SCA Check Software Composition Analysis (SCA) is a methodology that secures applications by analyzing their open-source components. SCA scanners look for outdated dependencies that contain known vulnerabilities and exploits. In addition, some SCA tools can determine the license compatibility of components and the risks of license infringement. Source SCA analyses the source code and, more specifically, the application dependencies defined in the source code. This analysis is often referred to as Dependency Scanning. SCA allows you to detect an issue where a vulnerability in one component can lead to security problems in all applications. A good example is . This vulnerability was discovered in late 2021 in Log4j, a popular Java logging framework. It became one of the most feared vulnerabilities because it allowed attackers to execute arbitrary Java code on hundreds of millions of devices. Log4Shell SCA tools is an open-source solution. Trivy Commercial solution with free pricing plans: FOSSA Mend SCA As part of GitLab (available in Ultimate-version only), you can use . GitLab Dependency Scanning Post-Build Checks: Binary SCA The post-build phase comes after artifacts have been built from source code in the CI Pipeline: a Docker image, an RPM package, or a JAR archive. With post-build checks, you can analyze the dependencies in these binary artifacts. Binary SCA analysis involves inspecting binary artifacts such as Docker images, RPM packages, and JAR/WAR/EAR archives. It is also often referred to as Container Scanning. It makes sense to run it when the binary artifacts are built, i.e., not before the post-build stage. There are some exciting features when working with Docker images. Binary SCA analyzers check layers of Docker images and look for them as hash sums in public vulnerability databases such as or . National Vulnerability Database (NVD) Snyk Vulnerability DB Some of the analyzers can not only find vulnerable components but also suggest a fix, for example, by specifying the minimum required version of a component with an already fixed vulnerability. Examples of Popular Binary SCA Analyzers The free solution is . GitLab Container Scanning Open Source solutions: Trivia Clair Grape Quay JFrog Xray Docker Hub Docker Registry with built-in analyzers - . Harbour Test-Time Checks: DAST, IAST, and OAST At this stage, dynamic Gray Box Testing and Black Box Testing are performed. When the application's internal structure is partially or wholly unknown, these security checks are performed by emulating the actions of an attacker who finds vulnerabilities and tries to "break" the application by exploiting them. Let`s briefly discuss each of them: DAST, IAST, and OAST. Test-Time DAST Check DAST scanners automatically test applications by simulating external attacks through various vulnerabilities. The application is a black box for the analyzer; nothing is known about it. For DAST checks, it is necessary to have a running instance of the application available for the scanner. Moreover, the closer the parameters of the test instance of the application are to the production installation, the fewer false positives there will be. For example, suppose you have deployed a test application instance accessible only via HTTP protocol, but in production, the application is accessible only via HTTP protocol. In that case, the DAST scanner will generate some errors related to the lack of traffic encryption between the client (analyzer) and the server (application instance). Examples of DAST Tools Tools that are worth your attention: — only available in the Ultimate version GitLab DAST — an open-source solution, which is also used in GitLab DAST OWASP Zed Attack Proxy (ZAP) Acunetix Fortify WebInspect HCL Security AppScan Synopsys Managed DAST (Web App Scanning) Tenable.io Veracode Dynamic Analysis Great, move on. Test-Time IAST Check IAST (Interactive Application Security Testing) is a Gray Box Testing that combines the advantages and strengths of White Box Testing SAST and Black Box Testing DAST. To collect all the advantages and eliminate the disadvantages of SAST and DAST methods, developers invented IAST - a hybrid mechanism combining these methods. It increases the accuracy of dynamic testing because it gives more information about the application operation through code analysis. It is worth remembering that besides its advantages, IAST has limitations. The most basic one is the dependence on the language in which the application under test is written. IAST solutions work at the application level and require access to the executable code to analyze its behavior. This means that IAST solutions must be able to understand the programming language in which the application is written. It should be noted that support for a particular IAST solution may be better implemented for some languages than others. IAST analysis also takes a long time. It depends on many factors, such as the size and complexity of the application, the number of external dependencies, and the performance of the IAST tool used. Besides, the analysis process does not scan the code itself but checks only those fragments that are directly executed. Therefore, IAST analysis is best combined with functional testing when you can test as many application scenarios as possible. Examples of IAST Tools Here are great tools for you: Synopsys Seeker Veracode Interactive Analysis Microfocus Fortify WebInspect Checkmarx CxIAST Contrast Assess Hdiv Detection (IAST) Great, move on. Test-Time OAST Check OAST (Out-of-band Application Security Testing) is a technique developed by and is an extension of DAST that finds vulnerabilities that DAST does not see, such as log4shell. PortSwigger Examples of OAST tools I recommend you: Burp Suite OWASP ZAP with OAST plugin OAST services , , and BOAST TukTuk interact Move on. Post-Deploy Checks This is an essential stage in ensuring application security and operability. Unlike pre-commit checks, which are performed at the development stage, post-deploy checks allow you to identify possible problems already during application operation in the natural environment. Monitoring the Safety of Artifacts For detecting new vulnerabilities in time, it is necessary to perform regular artifact checks, including after an application has been deployed. This can be done using special repositories or tools, such as , Trivy, GitLab Container Scanning, or developing your integration module. Snyk Container Application Security Monitoring Another way to ensure security is to monitor the application itself. There are several technologies available for this purpose: Web Application Firewall (WAF) is a traffic filtering tool. It works at the application layer and protects web applications by analyzing HTTP/HTTPS traffic. RASP (Runtime Application Self-Protection) is a technology that detects and blocks attacks on an application in real time. It adds a defense function to the runtime environment, allowing the application to self-protect automatically. The main advantage of RASP over WAF is that it understands how the application works and detects attacks and illegitimate traffic. RASP can see not only typical attacks using signature matching but also attempts to exploit zero-day vulnerabilities by reacting to anomalies. Unlike WAF, RASP works within and with the application. WAF can be fooled. If an attacker changes the application code, WAF cannot detect it because it does not understand the execution context. RASP has access to diagnostic information about the application, can analyze it, detect anomalies, and block attacks. RASP technology has a specialty of focusing on preventing attacks by default. The system does not require access to the source code. RASP Fools I recommend you: Fortify Sqreen is an open-source solution developed by Baidu OpenRASP Signal Sciences Jscrambler Hdiv Imperva Great, move on. Visualization of DevSecOps P ipelines Results and Vulnerability Management At different stages of the Pipeline, I use many Application Security Testing/DevSecOps analyzers. Each of them generates a report in its own format, and some of them generate so-called False Positives. Because of this, it isn't easy to analyze the security of an application as a whole. To effectively analyze vulnerabilities and manage the remediation process, specialized Vulnerability Management tools, often referred to simply as , are used. Security Dashboards Security Dashboards solve the problem by delivering the results of DevSecOps analyses in a unified and easy-to-analyse form. In this way, security engineers can see what issues exist, which ones are most critical, and which ones need to be addressed first. DevSecOps Tools A wide range of tools are usually used as Security Dashboards: for example, classic SOAR/SIEM systems and the specialized DevSecOps orchestrator AppSec.Hub from Swordfish Security or the open-source vulnerability management toolkit DefectDojo. DefectDojo is a widespread tool. It is widely used even in enterprise companies. However, despite its popularity and solid age, this tool occasionally has some initial-level defects when contributors break the basic functionality in the commit. However, what is nice is that DefectDojo developers and maintainers help to sort out the complexities. DefectDojo developers are very responsive people - we recommend contacting them directly via Issues on GitHub. Shift Left Concept: Let's Summarize Once again, here is a quick recap of what was in the article. I explained what the Shift Left concept is and how it helps reduce the cost of bug fixes and development time: the earlier in the development process you start security checks, the better. Then, I deconstructed the DevSecOps Pipeline structure and looked at what security checks are performed at each stage of the Pipeline: Pre-commit. It implies checking the safety of source code before it enters the version control system. Pre-build. It is a check of source code security in the version control system (Secret Detection, SAST, SCA). Post-build. It is a security check of an artifact built from source code (Source SCA, Binary SCA). Test-time. It implies testing the security of the application that is running from the collected artifact (DAST, IAST, and OAST). Post-deploy. It implies checking application security after deployment in the production environment (WAF, RASP). Now, you understand how the DevSecOps Pipeline works. Now, you can assess the maturity of your DevSecOps pipelines, develop a roadmap for its development, and choose the right tools for each task.

How to Turn A DevOps Pipeline Into a DevSecOps Pipeline: A Shift Left Concept Overview

কীভাবে একটি DevOps পাইপলাইনকে একটি DevSecOps পাইপলাইনে পরিণত করবেন: একটি শিফট বাম ধারণা ওভারভিউ

So verwandeln Sie eine DevOps-Pipeline in eine DevSecOps-Pipeline: Eine Übersicht über das Shift-Left-Konzept

Cómo convertir una canalización de DevOps en una canalización de DevSecOps: una descripción general del concepto de cambio a la izquierda

Comment transformer un pipeline DevOps en pipeline DevSecOps : un aperçu du concept Shift Left

एक DevOps पाइपलाइन को DevSecOps पाइपलाइन में कैसे बदलें: एक शिफ्ट लेफ्ट कॉन्सेप्ट अवलोकन

DevOps パイプラインを DevSecOps パイプラインに変える方法: シフトレフトの概念の概要

DevOps 파이프라인을 DevSecOps 파이프라인으로 전환하는 방법: Shift Left 개념 개요

Como transformar um pipeline DevOps em um pipeline DevSecOps: uma visão geral do conceito Shift Left

Как превратить конвейер DevOps в конвейер DevSecOps: обзор концепции сдвига влево

DevOps Boru Hattını DevSecOps Boru Hattına Dönüştürme: Sola Kaydırma Konseptine Genel Bakış

Cách biến quy trình DevOps thành quy trình DevSecOps: Tổng quan về khái niệm chuyển hướng sang trái

如何将 DevOps 管道转变为 DevSecOps 管道：左移概念概述

In this post we look at some of the most popular open-source tools for and verification. FPGA design Traditionally, when we create an FPGA design we have to use proprietary software tools to simulate and build our design. For example, when we create a design that targets a we would typically use . Xilinix FPGA Vivado software This means that when we target a different FPGA vendor we, also have to learn how to use a different tool. While this may not sound like much of a problem, it often takes time to get comfortable with the features of the software as well as the scripting languages associated with them. However, several open-source tools have been developed in the last few years which are aimed at simulating and building FPGA designs. Although these tools still have a long way to go before they support every FPGA on the market, they already offer support for a number of FPGAs from and Xilinx. Lattice As a result of this, we can often use the same open-source toolchain for many different FPGA projects. In addition to this, a lot of effort is being made to support more devices and introduce extra features. All of this means that open source FPGA tools are quickly advancing to the point where they can compete with commercially developed software. In the rest of this post, we look at some of the most commonly used open-source software tools which we can use when designing FPGAs. Simulation Tools There are several open-source FPGA tools which have been designed as an alternative to expensive software such as and . QuestaSim Synopsys VCS One of the main advantages of using open source simulators is undoubtedly the price. Whilst most vendors offer some form of free simulation tool, we tend to get useful extra features when we use open source simulators instead. For example, we often find better support for simulation libraries such as UVM with open source tools. In addition to this, open-source simulators are often simpler to use than their commercial alternatives. Despite these advantages, we must also consider some of the downsides to open source simulators. Perhaps the biggest drawback to using open source tools is that they tend to be slower than commercial tools. The exception to this rule is Verilator, which offers performance that is comparable to many commercial simulators. However, we still get better performance from a top of the range commercial simulator than we do from Verilator. Another problem we have when we use open-source simulators is that they don't support encrypted IP blocks. This is a problem if we want to use IP from vendors such as Xilinx as all of the simulation models which they supply are encrypted. Finally, there are currently no open-source simulators that offer multi-language support. As a result, we will not be able to use an open-source simulator when we create a design that uses both VHDL and verilog. While this is not generally a problem, we may have instances where we wish to design our FPGA using VHDL and create a testbench using SystemVerilog. We can't use this approach with the currently available open-source simulators. Let's take a look at some of the most popular open-source simulators. Icarus Verilog The software tool is an open-source verilog compiler that includes a synthesizer and simulator. Icarus Verilog We can use Icarus Verilog on Linux, macOS and Windows operating systems. Icarus Verilog supports all features of the , as well as a limited amount of SystemVerilog. verilog 2005 standard By default, we can't view waveforms from our simulation when we use Icarus Verilog. However, we can export our simulation results into the open-source when we need to view the waveforms. GTKWave program Icarus Verilog is one of the most popular open-source verilog simulators. In fact, it is so popular that it is even one of the featured simulators on . EDA playground One of the reasons for this is that it offers extremely good support for all features of the verilog 2005 standard. In addition to this, a community of developers actively maintain Icarus Verilog and regularly update it with new features. Simulating Designs with Icarus Verilog When we use Icarus Verilog to simulate our designs, we have to take two steps. Firstly, we have to compile our design into an object which the internal simulator can use. We can do this using the iverilog command line program. The code snippet below shows the general syntax we use to do this. iverilog -o <output_name> <input_name> In the above example, we use the <output_name> field to give a name to the compiled output. We use the <input_name> field in the above example to specify the verilog file that we want to compile. After we have compiled our verilog code we then need to call the simulator using the vvp command line program. The code snippet below shows the general syntax we use to do this. vvp <filename> Verilator The tool is an open-source verilog compiler that only accepts synthesizable verilog or SystemVerilog code. Verilator Unlike Icarus Verilog, we use Verilator to compile our verilog code into another programming language - either or . C++ SystemC As a result of this, we can't directly simulate the compiled objects which Verilator creates. Instead, we have to write some SystemC or C++ code which we can then use to simulate our design. We can then use a standard C compiler, such as , to compile our SystemC/C++ design into an executable file that we can run. GCC As we can see from this, when we are looking for a basic verilog simulator it is generally simpler to use Icarus Verilog. However, Verilator offers a number of advantages in comparison to other open-source and commercial compilers. Perhaps the biggest advantage of using Verilator to simulate our designs is speed. In comparison to Icarus Verilog or GHDL, our simulations will run much quicker when we use Verilator. In fact, Verilator also performs better than most free tools which are provided commercially by companies such as Xilinx and Intel. However, we will generally find that expensive paid simulators, such as QuestaSim, offer better performance than Verilator. Another advantage of Verilator is that it enforces strict rules. When we write code that can't be synthesized, Verilator will raise errors and tell us what is wrong with our code. linting As a result, we will be able to find bugs in our design much earlier in the design process. GHDL The software is an open-source VHDL compiler and simulator which has been around for nearly 20 years. GHDL In addition to this, it also features some VHDL synthesis capabilities, although this is currently not fully implemented. In many senses, we can consider GHDL to be the VHDL equivalent of the Icarus Verilog tool. We can use GHDL on virtually any operating system, including Linux, Windows and macOS. GHDL currently offers full support for the VHDL-87, 93 and 2002 standards. In addition, there is also partial support for the VHDL-2008 standard. As with Icarus Verilog, we can't view waveforms from our simulation when we use GHDL. However, we can export our simulation result to the open-source GTKWave program when we want to view the waveforms. GHDL is the most popular open source VHDL simulation tool. In fact, it is so popular than it is even one of the featured simulators on . EDA playground One of the main reasons for its popularity is the fact that it offers reliable support for many useful features of VHDL-2008. This is directly comparable to many commercially available simulators, most of which still don't fully support VHDL-2008. In addition to this, there is a community of developers which maintains the code on . As a result, we regularly get new features and bug fixes for the software. github Finally, we can also use popular simulation libraries such as or in GHDL. UVVM OSVVM Simulating Designs with GHDL When we want to simulate a design using GHDL, we have to take three separate steps. Firstly, we have to run an analysis stage. We use this step to convert our VHDL source code into a format that is used internally by GHDL. The code snippet below shows the command we use to run the analysis step. ghdl -a <input_name> In this example, we use the <input_name> field to specify which VHDL file we want to run the analysis on. Once we have run the analysis step, we can then compile our design into machine code using the elaboration step. The code snippet below shows the command we use to run the elaboration step. ghdl -e <input_name> In this example, we use the <input_name> field to specify which design we want to elaborate. Once we have compiled our VHDL source code into machine code, we can then run our simulation. The code snippet below shows the general syntax we use to run the simulation. ghdl -r <test_name> In this example, we use the <test_name> field to specify which simulation we want to run. We can also skip the elaborate step as it is also run as a part of the run command. However, we get extra warnings about our design when we run the elaborate step separately. This can be useful for finding bugs in our design. Tools for Building FPGA Designs There are now a number of open source tools that we can use to convert our source code into a bitstream. This includes tools that allow us to synthesize our verilog or VHDL code, perform place and route operations and create a bitstream to program into our target FPGA. However, most of these tools are fairly new developments that have appeared in the last ten years. Despite this, we have already started to see open-source tools find use with industrial customers. In particular, the toolchain for building several is now entirely open source. Lattice FPGAs In addition to this, large, coordinated projects such as are helping to accelerate the development of these tools. symbiflow Let's look at some of the most popular open-source tools which we can use to build FPGA bitstreams. Yosys Synthesis Tool is an open-source verilog tool that supports almost all features of the verilog 2005 standard. Yosys synthesis Thanks to the fact that it is highly customizable and , Yosys is the most popular open-source FPGA synthesis tool. extensible Yosys is actually a collection of related software tools which can be used to build a complete synthesis tool. As a result of this, we can easily customize the synthesis process depending on our needs. For example, we may want to use a different process for high-speed designs and low-speed designs. We use based scripts to customize the synthesis flow for different FPGAs. This approach means that it is actually relatively simple to customize Yosys for different requirements. tcl However, we can also simply use generic synthesis flows when we require a basic synthesis flow. In addition to this, we can also customize the Yosys source code if we wish to add extra features to the tool. After synthesizing our design using Yosys, we can also export the netlist to several different file formats, such as blif, JSON or edif. This is beneficial as it means that we can export our netlist to different place and route tools, depending on our exact toolchain. In addition, we can also generate our netlist in verilog. This allows us to carry out post-synthesis simulations on our design. At the moment, we can only use Yosys to synthesize FPGAs from the or families. However, the developers are making efforts to support more FPGA families. Lattice iCE40 Xilinix 7 series One disadvantage of the Yosys synthesis software is that it doesn't currently support VHDL based designs. However, we can use the open-source tool as a front end for Yosys when working with VHDL. This approach means that we generate a simple netlist using GHDL and then use Yosys to optimize it. GHDL The only downside to this approach is that the synthesis support in GHDL is still experimental. VPR Place and Route Tool The (VPR) tool is an open-source place and route tool which was first developed at the University of Toronto over 20 years ago. Versatile Place and Route We can use the VPR tool to perform placement, routing and analysis operations on our netlist. We can use synthesis tools such as Yosys to generate our netlist and then export it into a VPR project. Alternatively, we can also use the package which includes both VPR and a separate synthesis tool. Verilog-to-Routing We can use VPR on Linux and macOS based operating systems. In addition, we can also use in order to run VPR on windows. cygwin VPR was originally developed to provide a reliable software tool that can be used for testing out new routing algorithms. As a result of this, we mainly see VPR being used in academic and research projects. This means that it can be difficult to use when we want a simple way of building our code for readily available FPGAs. This feature means it is not really suitable for use in a simple VHDL/verilog-to-bitstream toolchain. Arachne Place and Route Tool The is an open-source place and route tool which was first released in 2015. Arachne PNR tool We can use Arachne to perform place and route operations for the Lattice ice40 family of FPGAs. Currently, this software does not support any other FPGA families. Arachne was the first open-source place and route tool which was used in a verilog to bitstream toolchain with industrial users. Whilst this represented a major milestone for open-source FPGA tools, there are several limitations to this software. As we have already mentioned, we can only use Arachne with the Lattice ice40 family of FPGAs. This means that we can only use Arachne with a very small subset of FPGA devices. In addition to this, Arachne uses a routing algorithm with is not timing driven. This means that the tool doesn't prioritize achieving the fastest maximum clock frequency when it performs the place and route operations. As a result of this, we can typically achieve better timing results with the same source code when we use a different place and route tool. Although we can still use Arachne, the code base is no longer actively maintained. As a result, we should use the nextpnr tool rather than Arachne for new projects. Nextpnr Place and Route Tool The tool is an open-source place and route tool which was first released in 2018. It was originally developed as a replacement for the Arachne place and route tool. nextpnr Since its release, it has grown to become the most popular open-source place and route tool. We can use nextpnr on Windows, macOS and Linux based systems. In addition to this, we also get a GUI interface as a part of the software. This is in contrast to all the other tools we have looked at so far which only have a command line interface. This can be a useful feature as it allows us to more easily debug our design by visualizing the circuit in the FPGA. Currently, nextpnr is primarily aimed at Lattice FPGAs with full support being offered for the ice40, ECP5 and Nexus families. However, work is currently being carried out to support more FPGAs in the future, in particular the Xilinx 7 Series family of FPGAs. Unlike Arachne, nextpnr uses a timing driven routing algorithm. This means that nextpnr priorities achieving the highest possible clock frequency for a given design. As a result of this, we can achieve higher frequency designs when we use nextpnr. However, the downside to this is that it typically takes longer to place and route a design. As we discussed with Yosys, we can also easily extend the functionality of the nextpnr tool as it is open source. In fact, this is even easier to do with nextpnr as it features a python interface as well. Nextpnr is currently the most popular open-source place and route tool by some margin. As a result of this, it is also used in popular verilog to bitstream projects such as and . symbiflow apio Bit Stream Generation The final stage in creating an FPGA programming file is generating the actual bitstream itself. As most FPGA vendors keep the contents of their bitstreams a secret, there are fewer open-source tools available for the generation of bitstreams. However, there are currently several projects which are aiming to document the format of these bitstreams. At the moment, there is full documentation available for the Lattice ice40 ( ) and ECP5 ( ) families of FPGA. project icestorm project trellis We can also use tools included in these projects to generate bitstreams which we can program into our target FPGA. There is also some documentation for the Xilinix 7 series family ( ) although this is not currently complete. project x-ray Also Published here

Getting Started Using Open Source FPGA Tools

The hype around AI is growing rapidly, as most research companies predict AI will take on an increasingly important role in the future. While business leaders are very interested in leveraging machine learning technology, there’s a standing in the way. talent shortage It turns out that there are very few developers that have the skills needed to spearhead serious new AI projects. This means that developers who can acquire these skills will be highly in demand. With all this in mind, let’s take a look at several facts about AI every developer should know before changing their focus to machine learning, artificial intelligence, and—while we’re at it—deep learning and neural networks. 1. Artificial intelligence is older than you, probably. The first recorded use of the term artificial intelligence was made by John McCarthy, an American computer scientist and one of the discipline's founders. Spending most of his academic career at Stanford, he invented in the late 1950s. Based on the , Lisp soon became the programming language of choice for AI applications after its publication in 1960. Lisp lambda calculus Still, creating AI departments at Stanford and MIT didn't advance the field as much as the founders had imagined. In large part, this is because scientists encountered a myriad of issues, including limited computer power (i.e., the memory or processing speed needed to accomplish anything truly useful), , the , lack of databases, and lack of the common sense knowledge and reasoning needed to train algorithms effectively. intractability combinatorial explosion The so-called AI winter began in the 1970s, and AI eventually reached its limits as substantial funding was put on hold. It was only in the 2000s that computational power and data became widely available. The ice was finally broken by , a database project that stored 15 million images led by Stanford's Fei Fei Li in 2009. At the same time, data storage rates became affordable, setting the stage for more AI investments. ImageNET 2. The talent pool is shallow. Talent is in short supply in the A.I. industry, with various reports indicating that the worldwide market is seeking to fill millions of roles. Due to a widespread lack of education on AI skills and topics, there’s a bottleneck in delivering highly trained individuals. In fact, Element AI, a Montreal-based startup, estimated in the world with the expertise needed to create machine learning systems. fewer than 22,000 people What’s more, another study made by the Chinese Tencent Research Institute estimates that there are currently in the world today, out of which about 100,000 are still studying. Tencent claims the United States is far ahead when it comes to developing this talent, being home to more than 1,000 universities of the 2,600 schools teaching machine learning and related subjects in the world. 300,000 AI researchers and practitioners The same report claims the U.S. is also a leading nation when it comes to the number of startups developing AI technologies. Interestingly enough, more and more academic conferences turn into playgrounds for corporate recruiters, while entire AI research departments from well-known universities are transferred to privately held companies deploying AI. 3. AI Engineers get paid very well Scarcity in any job market equates to higher salaries, and AI is no different. For example, DeepMind, acquired by Google for a reported $650 million in 2014, spends $138 million on 400 employees. The staff costs were researched by The New York Times, which had a look at the company's recently released annual financial accounts in the U.K. This translates to base salaries of between $300,000 and $500,000 a year. Based on Monster.com's analysis, the median salary for data scientists, senior data scientists, artificial intelligence consultants, and machine learning managers was $127,000 in 2019. Over the last four years, the demand for AI talent has increased by 74%, while technology and financial service companies are currently . absorbing 60% of AI talent 4. AI/ML Professionals need to possess a lot of skills There are two job roles for every AI professional today, and similar growth is expected to be seen in the future. Currently, the three most in-demand AI positions on the market are data scientists and algorithm developers, machine learning engineers, and deep learning engineers. According to the job site Indeed, the main skills and tools software developers need to be proficient on AI projects include math, algebra, statistics, big data, data mining, data science, machine learning, cognitive computing, natural language processing (NLP), Hadoop, Spark, and many others. The most frequent programming languages AI developers use are Phyton, , , , and . Still, qualifying job seekers must also be experienced working with open source development environments. For example, proficiency with Spark, MATLAB, and Hadoop is one of . C++ Java LISP Prolog the most in-demand skills 5. The hype around AI is worth it. In 2018, predicted that 80% of emerging technologies would have AI foundations within three years. What’s more, the research firm Markets and Markets expects that the AI market will grow to a . Beyond that, Accenture predicts that the impact of AI technologies on business will . Also, according to IDC, the AI use cases that saw the most investment in 2019 were automated customer service agents ($4.5 billion worldwide), sales process recommendation and automation ($2.7 billion), and automated threat intelligence and prevention systems ($2.7 billion). Gartner $190 billion industry by 2025 boost labor productivity by up to 40% Add it all up, and the hype surrounding AI is worth it. 6. AI has all kinds of implications Before wondering whether AI will replace software developers, let's take a look at what AI can actually do. The industries and use cases where AI can be deployed have surged in the past few years. In December 2018, the New York auction house Christie's sold Portrait of Edmond de Belamy, an algorithm-generated print in the style of 19th-century European portraiture, for $432,500. Various AI-generated works of art are now frequently exhibited; one example is the "Faceless Portraits Transcending Time" collection in New York. Dr. Ahmed Elgammal and his creation, the AICAM AI, benefit from the devoted to an AI artist. As Andy Warhol once said, art is what you can get away with. first solo gallery exhibit The frenzy around AI-generated art is also touching the music industry. While continuing to read these words, play using roughly 500 megabytes worth of famous guitar tabs of mostly classical and rock music. It's called Recurrence, and—if it's not contemporary enough for your taste—please note this "record" is already five years old. this piece generated in ASCII With a more substantial societal impact, AI tools are also being used to solve medical issues. AI is also being used in medical research to identify, prevent, and cure disorders and diseases is more appealing. These applications are projected to create for the healthcare economy by 2026. $150 billion in annual savings AI-based typing patterns matching algorithms can also verify users' identity based solely on their typing behavior. In 2016, technology was launched to analyze how humans interact with keyboards to provide accurate authentication. The breakthrough discovery here relies on the fact that humans are all different and behave in a distinctive way. The of how it works can turn into an addictive challenge game among your friends trying to "fool" the system and replicate each other's typing behavior. TypingDNA’s demo Further, Google's Deep Learning machine learning program is accurate in detecting breast cancer compared to a human pathologist, who is just 73 percent accurate. This is why machine learning and AI are regarded as healthcare’s new nervous system. 89 percent of the time Finally,  AI is also very smart, shedding light on its future capabilities. For example, AlphaGo Zero, a Google Deep mind project, was able to achieve champion predecessor, AlphaGo, the first AI to defeat Ke Jie, the world's top-ranked player in the ancient Chinese strategy game Go. Interestingly, AlphaGo Zero taught itself how to play the game, given only the basic rules. superhuman-level performance, flawlessly beating 7. AI won’t replace human beings but their jobs. Twenty-five years ago, Jeff Dean started working on a "brain" that mimicked the neural networks to analyze information and learn. But its capabilities were limited. It was only in 2012 that neural networks were successfully used for machine learning, memory, perception, and symbol processing. Geoff Hinton marked a new era when introducing neural networks that could learn tasks mostly on their own by analyzing vast amounts of data. Both Dean and Hinton are now part of Google's AI research teams. In 2017, Google announced its project successfully taught itself to program machine learning software on its own. Completing basic programming tasks, AutoML also marked the popularization of a new fear: Because of their ability to learn on their own, will machines replace humans? AutoML Welcome to this century's agnostophobia. Unlike the Narrow/Weak AI,  specified to handle singular or limited tasks humans can perform as well, General or Strong AI poses fears towards its capabilities once out of control. Currently, AI is used mostly to assist developers, and it will probably continue to grow its role in human teams' capabilities. We see it everywhere around us—in tools to help write documentation, test code, and even identify bugs and address them. augmenting Open AI and it’s recent Generative Pre-trained Transformer 3 (GPT-3), an autoregressive language model with 175 billion parameters, achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation. This means it has capabilities to generate news articles in which human evaluators have difficulty distinguishing from articles written by humans while claim GPT-3 has the "potential to advance both the beneficial and harmful applications of language models." researchers Researchers at MIT that automatically fixed software bugs by replacing faulty code lines with working lines from other programs. Here are some more tools that help in the process of building software products: , , and . created a program DeepCode Synopsys Logojoy UIzard 8. How do developers look at AI and its potential threats? If you fear that AI will eventually replace your role, you're not alone. That’s how the majority of developers around the world feel. According to , when asked to identify the most worrisome thing in their careers, the largest plurality of software developers cited this: "I and my development efforts are replaced by artificial intelligence." Evans Data On a positive note, Stack Overflow research showed that 70% of respondents feel more excited about AI's possibilities instead of worrying about its potential dangers. And most developers are eagerly looking forward to the new possibilities automation brings to the table. Just like the industrial revolution shifted humankind into developing new skills while leaving agricultural labor behind, so will intelligent robots. In fact, McKinsey predicts AI could replace 30% of the human workforce globally by 2030. According to AI technology statistics, robotics could replace about 800 million jobs, making about 30% of occupations extinct. With this significant shift, nearly 400 million people will have to adapt and change careers. predicts cognitive technologies—such as robots, A.I., machine learning, and automation—will create 9% of new U.S. jobs by 2025. These new jobs include robot monitoring professionals, data scientists, automation specialists, and content curators. Forrester 9. It’s easy to start learning or teaching Since the skilled workers needed to build advanced AI software are still scarce, companies like and have prepared educational programs designed to get anyone on board—no matter their level of expertise. If you are interested in online courses to grasp the basics of A.I., check these machine learning courses from , and or dive into the depths of deep learning at . Facebook Google Stanford MIT, Columbia University, Nvidia's Deep Learning Institute For more information, you can also , check out , and browse . This will help you build the technology of the future and solve real-world problems. read this book these popular open-source AI tools this list of popular AI projects And if you’re a teacher looking to introduce your students to AI, check out Tom Vander Ark’s compelling guide on . how to teach artificial intelligence The graphics in this article can be downloaded here .

AI Facts Every Dev Should Know: Artificial intelligence is older than you, probably

Alan Kay said: “Simple things should be simple, complex things should be possible”. This sentence was the inspiration for the founder of RT-Thread to stick to his beliefs. Background: RT-Thread was born in 2006, it is an open source, neutral, and community-based real-time operating system (RTOS). RT-Thread has Standard version and Nano version: For resource-constrained microcontroller (MCU) systems, the NANO kernel version that requires only 3KB Flash and 1.2KB RAM memory resources can be tailored with easy-to-use tools; For resource-rich IoT devices, RT-Thread can use the on-line software package management tool, together with system configuration tools, to achieve intuitive and rapid modular cutting, seamlessly import rich software packages, thus achieving complex functions like Android's graphical interface and touch sliding effects, smart voice interaction effects, and so on. RT-Thread has not only a real-time kernel, but also rich components. Its architecture is as follows: RT-Thread Architecture: It includes: Kernel Layer: RT-Thread kernel, the core part of RT-Thread, includes the implementation of objects in the kernel system, such as multi-threading and its scheduling, semaphore, mailbox, message queue, memory management, timer, etc.; libcpu/BSP (Chip Migration Related Files/Board Support Package) is closely related to hardware and consists of peripheral drivers and CPU porting. Components and Service Layer: Components are based on upper-level software on top of the RT-Thread kernel, such as virtual file systems, FinSH command-line interfaces, network frameworks, device frameworks, and more. Its modular design allows for high internal cohesion inside the components and low coupling between components. RT-Thread Software Package: A general-purpose software component running on the RT-Thread IoT operating system platform for different application areas, consisting of description information, source code or library files. RT-Thread provides an open package platform with officially available or developer-supplied packages that provide developers with a choice of reusable packages that are an important part of the RT-Thread ecosystem. The package ecosystem is critical to the choice of an operating system because these packages are highly reusable and modular, making it easy for application developers to build the system they want in the shortest amount of time. RT-Thread supports more than 200 software packages. RT-Thread Features: Supports resource-constrained devices, the minimum kernel requires only 1.2KB of RAM and 3 KB of Flash. It has rich components and a prosperous and fast growing package ecosystem. Elegant code style, easy to use, read and master. High Scalability. RT-Thread has high-quality scalable software architecture, loose coupling, modularity, is easy to tailor and expand. Supports high-performance applications. Supports cross-platform and a wide range of chips. RT-Thread supports many architectures, and has covered the major architectures in current applications. Architecture and chip manufacturer involved: Supported Architectures: ：manufacturers like ST. ARM Cortex-M0/M0+ ：manufacturers like ST、Winner Micro、MindMotion, etc. ARM Cortex-M3 ：manufacturers like ST、Nuvton、NXP、GigaDevice、Realtek、Ambiq Micro, etc. ARM Cortex-M4 ：manufacturers like ST、NXP ARM Cortex-M7 ：manufacturers like GigaDevice ARM Cortex-M23 ：manufacturers like NXP ARM Cortex-R4ARM Cortex-A8/A9 ：manufacturers like Samsung ARM7 ：manufacturers like Allwinner、Xilinx 、GOKE ARM9 ：manufacturers like Fullhan ARM11 ：manufacturers like loongson、Ingenic MIPS32 ：manufacturers like Hifive、Kendryte RISC-V ：manufacturers like SYNOPSYS ARC ：manufacturers like TI DSP C-Sky X 86 The main IDE/compilers supported by RT-Thread are: Supported IDE and Compiler: MDK KEIL IAR GCC RT-Thread Studio(RT-Thread studio is a one-stop development tool built by RT-Thread) Use Python-based for command-line builds. RT-Thread has received many many supports from the community developers when it starts. Now, RT-Thread has gathered 200+ software packages which are created by the community developers also RT-Thread had 9357 commits and gained 4.2K stars on Github. Real-time system (RTOS) is increasingly widely used in the high-end Internet of Things (like AIoT) because of its low cost, high real-time, and fast start-up characteristics, soon or later, more and more RTOSs would support multi-kernel SMP, AI, audio & video and this is inevitable. In the near future, RT-Thread Studio IDE, next-generation microkernel architecture, AI frameworks and more will all be released step by step. This is a new world of RTOS. scons Community: RT-Thread Contact Info： Website: https://www.rt-thread.io/ Github: https://github.com/RT-Thread Twitter: https://twitter.com/rt_thread Facebook： https://www.facebook.com/RT-Thread-IoT-OS-110395723808463/?modal=admin_todo_tour Youtube: https://www.youtube.com/channel/UCdDHtIfSYPq4002r27ffqPw?view_as=subscriber

An Introduction to Open-source Real-time Operating System

SYNOPSYS INC

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SYNOPSYS INC

synopsys.com

10K+ employees

Since 1986

COMPANY RANKING

SYNOPSYS INC

EVERGREEN INDEX #10204

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