Axelera AIis in the AI Infrastructure Industry

Axelera AI delivers an AI-native game-changing hardware and software platform to accelerate artificial intelligence. Headquartered in the AI Innovation Center of the High Tech Campus in Eindhoven, Axelera AI has R&D offices in Belgium, Switzerland, UK and Italy and operations in 15 European countries . Its team of experts in AI software and hardware hail from top AI firms and Fortune 500 companies.

Axelera AI

How does work? nebullvm It takes your AI model as input and outputs an optimized version that runs 5-20 times faster on your hardware. In other words, nebullvm tests multiple deep learning compilers to identify the best possible way to execute your model on your specific machine, without impacting the accuracy of your model. And that's it. In just a few lines of code. And a big thank you to everyone for supporting this open-source project! The library received 250+ Github stars⭐ on release day, and that's just amazing 🚀 Orientation Map Let's learn more about nebullvm and AI optimization. Where should we start? From... Some on why few developers optimize AI and related negative consequences CONTEXT An overview of how the works LIBRARY Some , technology demonstrations and benchmarks USE CASES A description of the behind the library TECHNOLOGY Or let's jump straight to the library → nebullvm Context Finally, the adoption of Artificial Intelligence (AI) is growing rapidly, although we are still far from exploiting the full potential of this technology. Indeed, what typically happens is that AI developers spend most of their time on data analysis, data cleaning, and model testing/training with the objective of building very accurate AI models. Yet... few models make it into production. If they do, two situations arise: AI models are developed by skilled data scientists and great AI engineers, who often have limited experience with cloud, compilers, hardware, and all the low-level matters. When their models are ready to be deployed, they select the first GPU or CPU they can think of on the cloud or their company/university server, unaware of the severe impact on model performance (i.e. much slower and more expensive computing) caused by uninformed hardware selection, poor cloud infrastructure configuration, and lack of model/hardware post-training optimization. Other companies have developed in-house AI models that work robustly. AI inference is critical to these companies, so they often build a team of hardware/cloud engineers who spend hours looking for out-of-the-box methods to optimize model deployment. Do you fall into one of these two groups? Then you might be interested in the nebullvm library, and below we explain why. Library How does nebullvm work? You import the library, nebullvm does some magic, and your AI model will run 5-20 times faster. And that's it. In just a few lines of code. The goal of nebullvm library is to let any developer benefit from deep learning compilers without having to waste tons of hours understanding, installing, testing and debugging this powerful technology. Nebullvm is quickly becoming popular, with 250+ GitHub stars on release day and hundreds of active users from both startups and large tech companies. The library aims to be: 💻 nebullvm supports all the most popular architectures such as transformers, LSTMs, CNNs and FCNs. Deep learning model agnostic. 🤖 . The library now works on most CPUs and GPUs and will soon support TPUs and other deep learning-specific ASICs. Hardware agnostic 🔥 . nebullvm supports the most widely used frameworks (PyTorch, TensorFlow and Hugging Face) and will soon support many more. Framework agnostic 🔑 Everything runs locally on your machine. Secure. ☘️ . It takes a few lines of code to install the library and optimize your models. Easy-to-use ✨ . There are tons of DL compilers that optimize the way your AI models run on your hardware. It would take tons of hours for a developer to install and test them at every model deployment. The library does it for you! Leveraging the best deep learning compilers Use cases Why is accelerating computing by 5-20x so valuable? To → Accelerate your AI services and make them real-time. save time To → Reduce cloud computing costs. save money To → Reduce the electricity consumption and carbon footprint of your AI services. save energy Probably you can easily grasp how accelerated computing can benefit your specific use case. We'll also provide you with some use cases on how nebullvm is helping many in the community across different sectors: Fast computing makes search and recommendation engines faster, which leads to a more enjoyable user experience on websites and platforms. Besides, near real-time AI is a strict requirement for many healthtech companies and for autonomous driving, when slow response time can put people's lives in danger. The metaverse and the gaming industry also require near-zero latency to allow people to interact seamlessly. Speed can also provide an edge in sectors such as crypto/NFT/fast trading. Lowering costs with minimal effort never hurts anyone. There is little to explain about this. Green AI is a topic that is becoming more popular over time. Everyone is well aware of the risks and implications of climate change and it is important to reduce energy consumption where possible. Widespread awareness of the issue is reflected in how purchasing behavior across sectors is moving toward greater sustainability. In addition, low power consumption is a system requirement in some cases, especially on IoT/edge devices that may not be connected to continuous power sources. Technology Demonstration We suggest testing the library on your AI model right away by following the installation instructions on . If instead you want to get a hands-on sense of the library's capabilities, check out the where you can test nebullvm on popular deep learning models. Note that notebooks will still require you to install the library as you will to test nebullvm on your models, which will take several minutes. Once it's installed, nebullvm will optimize your models in a short time. Github notebooks at this link Benchmarks We have also tested nebullvm on popular AI models and hardware from leading vendors. Hardware: M1 Pro, NVIDIA T4, Intel Xeon, AMD EPYC AI Models: EfficientNet, Resnet, SqueezeNet, Bert, GPT2 At first glance, we can observe that acceleration varies greatly across hardware-model couplings. Overall, the library provides great positive results, most ranging from 2 to 30 times speedup. To summarize, the results are: Nebullvm provides positive acceleration to non-optimized AI models Early results show poorer (yet positive) performance on Hugging Face models. Support for Hugging Face has just been released and improvements will be implemented in future versions Nebullvm provides a ~2-3x boost on Intel hardware. These results are most likely related to an already highly optimized implementation of PyTorch for Intel devices Extremely good performances on NVIDIA machines The library provides great performances also on Apple M1 chips And across all scenarios, nebullvm is very useful for its ease of use, allowing you to take advantage of deep learning compilers without having to spend hours studying, testing and debugging this technology The table below shows the response time in milliseconds (ms) of the non-optimized model and the optimized model for the various model-hardware couplings as an average value over 100 experiments. It also displays the provided by nebullvm, where speedup is defined as the response time of the optimized model over the response time of the non-optimized model. speedup Hardware used for the experiment is the following: M1 Pro → Apple M1 Pro 16GB of RAM Intel Xeon → EC2 Instance on AWS - t2.large AMD EPYC → EC2 Instance on AWS - t4a.large Nvidia T4 → EC2 instance on AWS - g4dn.xlarge Technology Nebullvm leverages the best deep learning compilers to accelerate AI models in inference. So what exactly are deep learning compilers? A deep learning compiler takes your model as input and produces an efficient version of it that runs the model computation graph faster on a specific hardware. How? There are several methods that, in principle, all attempt to rearrange the computations of neural networks to make better use of the hardware memory layout and optimize hardware utilization. In very simplistic terms, deep learning optimization can be achieved by optimizing the entire end-to-end computation graph, as well as by restructuring operators (mainly for loops related to matrix multiplications) within the graph [ , ]. Here are some examples of optimization techniques: 1 2 . It refers to the process where a sequence of operators eligible for fusion is first identified and then replaced with a corresponding handwritten implementation. Fusing operators allows for better sharing of computation, removal of intermediate allocations, and facilitates further optimization by combining loop nests. [ ] Operator fusion 3 . It refers to techniques for performing computations and storing tensors at lower bitwidths than floating point precision. A quantized model executes some or all of the operations on tensors with integers rather than floating point values. [ ] Quantization 4, 5 . Pruning refers to removing certain parameters in the neural network because they are redundant and do not contribute significantly to the output, resulting in a smaller, faster network. [ ] Graph pruning 6 Deep learning optimization depends greatly on the specific hardware-software coupling, and specific compilers work best on specific couplings. So it is difficult to know a priori the performance of the many deep learning compilers on the market for each specific use case and testing is necessary. This is exactly what does, saving programmers countless hours. nebullvm Acknowledgements The team behind nebullvm are a group of former MIT, ETH, and EPFL folks who team up together and launched . They developed this open-source library along with a lot of other great technologies to make AI more efficient. You can find out more about Nebuly on its , , or . Nebuly website LinkedIn Twitter Instagram Many kudos go to , the library's main contributor. Diego is a curious person and always thirsty for knowledge, which he likes to consume as much as good food and wine. He is a versatile programmer, very jealous of his code, and never lets his code look less than magnificent. In short, Diego is the CTO of . Diego Fiori Nebuly Huge thanks also go to the open-source community that has developed numerous DL compilers that enable to accelerate AI models. And finally, many thanks to all those who are supporting the , finding bugs and fixing them, and enabling the creation of a state-of-the-art, this super-powerful AI accelerator. nebullvm open-source community References Papers and articles about deep learning compilers. [1] A friendly introduction to machine learning compilers and optimizers by Chip Huyen [2] The Deep Learning Compiler: A Comprehensive Survey by Mingzhen Li & Al [3] Principled optimization of dynamic neural networks by Jared Roesch [4] A Survey of Quantization Methods for Efficient Neural Network Inference by Amir Gholami & Al. [5] Quantization for Neural Networks by Lei Mao [6] Neural Network Pruning 101 by Hugo Tessier Documentation of deep learning compilers used by nebullvm. Apache TVM 's NVIDIA TensorRT 's Intel OpenVINO MLIR

This Open-Source Library Accelerates AI Inference by 5-20x in a Few Lines of Code

Esta biblioteca de código abierto acelera la inferencia de IA entre 5 y 20 veces en unas pocas líneas de código

यह ओपन-सोर्स लाइब्रेरी कोड की कुछ पंक्तियों में एआई अनुमान को 5-20x तक तेज करती है

Thư viện nguồn mở này tăng tốc độ suy luận của AI lên 5-20 lần trong một vài dòng mã

这个开源库在几行代码中将 AI 推理速度提高了 5-20 倍

When I first heard about Nervos Network's dedication to building a BTC Layer 2, I wasn't surprised. That's because the CKB public blockchain technology not only matches but also goes beyond BTC. It not only maintains BTC's native UTXO features but also allows for more complex programmable extensions. While surpassing BTC may seem challenging for CKB public chain, aiming to be a top-notch BTC Layer 2 seems like a smart choice. Why? Let's delve into my take on CKB. The current BTC Layer 2 market is constrained by the mainnet’s limited validation capabilities, resulting in diverse alternative solutions. The simplicity of BTC's scripting language, combined with its near-zero computation and validation power, has created significant opportunities for market innovation. As of now, aside from limited transaction validation and multisig functionalities within UTXO unlocking conditions, the BTC mainnet is incapable of directly handling more complex transaction logic involving data validation, state changes, etc. It relegates BTC to an asset settlement layer, relying on extending through a powerful public chain for local consensus and computational validation capabilities. This leads to a lack of uniform standards or 'orthodoxy' for BTC Layer 2 solutions, making it challenging to rank them. However, distinctions can be made based on the community's perception between narrow and broad interpretations: Narrowly defined, only solutions like the Lightning Network state channels and the one-time seal approach of RGB qualify as truly 'orthodox' BTC Layer 2s. They effectively utilize BTC's limited script validation capacity without relying on or minimally depending on external local consensus. In a more general sense, any extension chain that gains acceptance for its local consensus, along with a cross-chain bridge solution that ensures the safe migration of assets, can theoretically serve as a BTC Layer 2. This includes contemporary Ethereum EVM chains, Solana with its high-concurrency capabilities, and others. Clearly, the BTC Layer 2 market is divided: on one end are highly-specific solutions like the slowly evolving Lightning Network and RGB, facing substantial challenges; on the other end are broadly defined solutions, where any performance chain capable of securely interacting with the BTC mainnet qualifies as a BTC Layer 2. But is there a 'middle-of-the-road' option? Yes, and it lies in the Nervos Network, which adheres to the UTXO model at its core, enhancing its performance. Notable features include: The CKB Network aligns closely with BTC, sharing the same 'UTXO model and mining consensus mechanism,' unlike the account balance model of mainstream public blockchains like Ethereum. The UTXO model has distinct advantages in transaction privacy, flexible transaction structuring, and parallel processing capabilities to prevent double-spending, which may be one of Satoshi Nakamoto’s most brilliant inventions. This is why post-Ethereum projects like Sui and Aptos have also adopted similar UTXO models. Bitcoin's capacity and block speed may be constrained by its era, but the UTXO model is remarkably forward-thinking. CKB adopts this UTXO model, evolving it into the 'Cell model', which preserves the pure transactional nature of Bitcoin’s UTXO model while also supporting the data states found in account models like Ethereum’s. To simplify: In the Bitcoin UTXO model, the creation and destruction of coins resemble a continuous minting and melting process. The Cell model, however, omits the destruction aspect, focusing on verifying and persistently storing states. Each Cell includes Capacity and Data: Capacity measures the balance in bytes, similar to UTXO, while Data holds any kind of information, including the history of transaction states. Thus, the Cell model can accurately represent balances and manage asset transfers, and also handle a variety of complex smart contract states. In summary, the Cell model is a more persistent and flexible transaction model that greatly broadens the scope of the UTXO model. It is crucial for CKB’s ability to maintain the security of the BTC mainnet while offering a 'speed boost' to slower Bitcoin expansion initiatives like the Lightning Network and RGB. For instance, the recent rollout of RGB++ by CKB illustrates this. In the BTC ecosystem, developing a mature RGB solution involves challenges that are not so much about the one-time sealing process of the BTC mainnet, but rather the communication, coordination, and mutual state maintenance among off-chain client validation nodes, particularly in a decentralized setting. In simpler terms, RGB's theory might seem straightforward, but its practical implementation is hindered by foundational infrastructural limitations and various barriers.
Recognizing this, CKB integrates these off-chain client validation nodes into its on-chain public validation process. This approach significantly accelerates the intended UTXO client extension pathway that RGB aims to achieve. Complex P2P consensus among off-chain nodes is notoriously challenging, fraught with complexity and obstacles such as potential data synchronization delays or inconsistencies, and susceptibility to fraud and attacks. Transposing this process onto the blockchain can simplify these issues. With the increasing discussions around RGB++, let’s also look at CKB's Open Transaction data format, showcasing the chain's forward-thinking features. Open Transaction allows multiple participants to collaboratively build and aggregate different transactions over time. It supports partial construction, amendability, and incremental build-up and aggregation. For example, Alice initiates an Open Transaction to exchange a certain amount of Token A for Token B with Bob. The transaction, once started, remains in an editable state. Bob, upon agreeing to the transaction terms, can then add his Token B and finalize the conditions. It might initially seem abstract. Take cross-chain scenarios as an example: Alice and Bob could independently execute asset trades on various distinct chains, significantly boosting the CKB chain's cross-chain interaction capabilities. In the realm of complex DeFi transactions, where market-driven dynamic adjustments are often necessary, Open Transaction allows contract participants to adapt trading conditions fluidly during the contract's execution. This undoubtedly enhances the ability to manage transactional complexities. From my perspective, Open Transaction mirrors the UTXO transaction unlocking conditions, capable of amalgamating intricate unlocking conditions, multi-party signatures, and complex transactional environments. This represents an evolutionary and valuable innovation that builds on the foundational principles of the BTC main chain. Interestingly, Jan Xie, a core developer from the Ethereum team, chose to embrace the BTC UTXO model for his inaugural major project. Despite the broader application of Ethereum's smart contract model, Jan and his Nervos team decisively chose to expand and refine the BTC UTXO model. This choice reflects a deep respect for Satoshi Nakamoto’s simplistic UTXO transaction model and also subtly lays the groundwork for its transformation into a native BTC Layer 2. In conclusion, I am quite bullish about CKB’s potential as a BTC Layer 2. In the short term, it certainly has the potential to expedite the implementation of projects like the Lightning Network and RGB within UTXO-based chains, offering valuable insights for these 'orthodox' expansion solutions on the BTC mainnet. Looking long-term, the inherent features of CKB's chain and its innovative architectural compatibility may enable it to excel in the complex and standardless arena of BTC Layer 2s. Note: There’s much more to explore regarding the technical nuances and standout features of CKB, which I plan to analyze in greater depth later. It's fascinating to see how BTC Layer 2 not only provides a platform for new chains to rise but also opens up endless possibilities for rejuvenation within established chains. Author: Haotian, Independent Researcher This article is the translation of a Haotian’s tweet. When I first heard about Nervos Network's dedication to building a BTC Layer 2, I wasn't surprised. That's because the CKB public blockchain technology not only matches but also goes beyond BTC . It not only maintains BTC's native UTXO features but also allows for more complex programmable extensions. While surpassing BTC may seem challenging for CKB public chain, aiming to be a top-notch BTC Layer 2 seems like a smart choice. Why? Let's delve into my take on CKB. BTC The current BTC Layer 2 market is constrained by the mainnet’s limited validation capabilities, resulting in diverse alternative solutions. The simplicity of BTC's scripting language, combined with its near-zero computation and validation power, has created significant opportunities for market innovation. As of now, aside from limited transaction validation and multisig functionalities within UTXO unlocking conditions, the BTC mainnet is incapable of directly handling more complex transaction logic involving data validation, state changes, etc. It relegates BTC to an asset settlement layer, relying on extending through a powerful public chain for local consensus and computational validation capabilities. This leads to a lack of uniform standards or 'orthodoxy' for BTC Layer 2 solutions, making it challenging to rank them. However, distinctions can be made based on the community's perception between narrow and broad interpretations: However, distinctions can be made based on the community's perception between narrow and broad interpretations: Narrowly defined, only solutions like the Lightning Network state channels and the one-time seal approach of RGB qualify as truly 'orthodox' BTC Layer 2s. They effectively utilize BTC's limited script validation capacity without relying on or minimally depending on external local consensus. In a more general sense, any extension chain that gains acceptance for its local consensus, along with a cross-chain bridge solution that ensures the safe migration of assets, can theoretically serve as a BTC Layer 2. This includes contemporary Ethereum EVM chains, Solana with its high-concurrency capabilities, and others. Clearly, the BTC Layer 2 market is divided: on one end are highly-specific solutions like the slowly evolving Lightning Network and RGB, facing substantial challenges; on the other end are broadly defined solutions, where any performance chain capable of securely interacting with the BTC mainnet qualifies as a BTC Layer 2. Clearly, the BTC Layer 2 market is divided: But is there a 'middle-of-the-road' option? Yes, and it lies in the Nervos Network, which adheres to the UTXO model at its core, enhancing its performance. Notable features include: The CKB Network aligns closely with BTC, sharing the same 'UTXO model and mining consensus mechanism,' unlike the account balance model of mainstream public blockchains like Ethereum. The UTXO model has distinct advantages in transaction privacy, flexible transaction structuring, and parallel processing capabilities to prevent double-spending, which may be one of Satoshi Nakamoto’s most brilliant inventions. This is why post-Ethereum projects like Sui and Aptos have also adopted similar UTXO models. Bitcoin's capacity and block speed may be constrained by its era, but the UTXO model is remarkably forward-thinking. CKB adopts this UTXO model, evolving it into the 'Cell model', which preserves the pure transactional nature of Bitcoin’s UTXO model while also supporting the data states found in account models like Ethereum’s. To simplify : In the Bitcoin UTXO model, the creation and destruction of coins resemble a continuous minting and melting process. The Cell model, however, omits the destruction aspect, focusing on verifying and persistently storing states. Each Cell includes Capacity and Data: Capacity measures the balance in bytes, similar to UTXO, while Data holds any kind of information, including the history of transaction states. Thus, the Cell model can accurately represent balances and manage asset transfers, and also handle a variety of complex smart contract states. To simplify In summary, the Cell model is a more persistent and flexible transaction model that greatly broadens the scope of the UTXO model. It is crucial for CKB’s ability to maintain the security of the BTC mainnet while offering a 'speed boost' to slower Bitcoin expansion initiatives like the Lightning Network and RGB. For instance, the recent rollout of RGB++ by CKB illustrates this. In the BTC ecosystem, developing a mature RGB solution involves challenges that are not so much about the one-time sealing process of the BTC mainnet, but rather the communication, coordination, and mutual state maintenance among off-chain client validation nodes, particularly in a decentralized setting. In simpler terms, RGB's theory might seem straightforward, but its practical implementation is hindered by foundational infrastructural limitations and various barriers.
Recognizing this, CKB integrates these off-chain client validation nodes into its on-chain public validation process. This approach significantly accelerates the intended UTXO client extension pathway that RGB aims to achieve. Complex P2P consensus among off-chain nodes is notoriously challenging, fraught with complexity and obstacles such as potential data synchronization delays or inconsistencies, and susceptibility to fraud and attacks. Transposing this process onto the blockchain can simplify these issues. With the increasing discussions around RGB++, let’s also look at CKB's Open Transaction data format, showcasing the chain's forward-thinking features. Open Transaction allows multiple participants to collaboratively build and aggregate different transactions over time. It supports partial construction, amendability, and incremental build-up and aggregation. For example, Alice initiates an Open Transaction to exchange a certain amount of Token A for Token B with Bob. The transaction, once started, remains in an editable state. Bob, upon agreeing to the transaction terms, can then add his Token B and finalize the conditions. It might initially seem abstract. Take cross-chain scenarios as an example: Alice and Bob could independently execute asset trades on various distinct chains, significantly boosting the CKB chain's cross-chain interaction capabilities. In the realm of complex DeFi transactions, where market-driven dynamic adjustments are often necessary, Open Transaction allows contract participants to adapt trading conditions fluidly during the contract's execution. This undoubtedly enhances the ability to manage transactional complexities. From my perspective, Open Transaction mirrors the UTXO transaction unlocking conditions, capable of amalgamating intricate unlocking conditions, multi-party signatures, and complex transactional environments. This represents an evolutionary and valuable innovation that builds on the foundational principles of the BTC main chain. Interestingly, Jan Xie , a core developer from the Ethereum team, chose to embrace the BTC UTXO model for his inaugural major project. Despite the broader application of Ethereum's smart contract model, Jan and his Nervos team decisively chose to expand and refine the BTC UTXO model. This choice reflects a deep respect for Satoshi Nakamoto’s simplistic UTXO transaction model and also subtly lays the groundwork for its transformation into a native BTC Layer 2. Jan Xie Jan Xie In conclusion , I am quite bullish about CKB’s potential as a BTC Layer 2. In the short term, it certainly has the potential to expedite the implementation of projects like the Lightning Network and RGB within UTXO-based chains, offering valuable insights for these 'orthodox' expansion solutions on the BTC mainnet. Looking long-term, the inherent features of CKB's chain and its innovative architectural compatibility may enable it to excel in the complex and standardless arena of BTC Layer 2s. In conclusion Note: There’s much more to explore regarding the technical nuances and standout features of CKB, which I plan to analyze in greater depth later. It's fascinating to see how BTC Layer 2 not only provides a platform for new chains to rise but also opens up endless possibilities for rejuvenation within established chains. Author: Haotian, Independent Researcher Author: Haotian , Independent Researcher Author: Haotian Haotian This article is the translation of a Haotian’s tweet. This article is the translation of a Haotian’s tweet. This article is the translation of a Haotian’s tweet . tweet tweet

How CKB's Public Blockchain Tech Accelerates Bitcoin's Orthodox Layer 2  

কিভাবে CKB এর পাবলিক ব্লকচেইন টেক বিটকয়েনের অর্থোডক্স লেয়ার 2কে ত্বরান্বিত করে

Wie die öffentliche Blockchain-Technologie von CKB die orthodoxe Schicht 2 von Bitcoin beschleunigt

Cómo la tecnología blockchain pública de CKB acelera la capa 2 ortodoxa de Bitcoin

Comment la technologie de blockchain publique de CKB accélère la couche orthodoxe 2 de Bitcoin

सीकेबी की सार्वजनिक ब्लॉकचेन तकनीक बिटकॉइन की रूढ़िवादी परत 2 को कैसे गति देती है

CKBのパブリックブロックチェーン技術がビットコインのオーソドックスなレイヤー2を加速させる方法

CKB의 퍼블릭 블록체인 기술이 비트코인의 정통 레이어 2를 가속화하는 방법

Como a tecnologia Blockchain pública da CKB acelera a camada ortodoxa 2 do Bitcoin

Как публичная технология блокчейна CKB ускоряет ортодоксальный уровень Биткойна 2

CKB'nin Halka Açık Blockchain Teknolojisi Bitcoin'in Ortodoks Katman 2'sini Nasıl Hızlandırıyor?

Công nghệ Blockchain công cộng của CKB tăng tốc lớp 2 chính thống của Bitcoin như thế nào

CKB 的公共区块链技术如何加速比特币正统的第 2 层

AXELERA AI

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51-200 employees

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AXELERA AI

axelera.ai

51-200 employees

Since 2021

COMPANY RANKING

AXELERA AI

EVERGREEN INDEX #2235

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