Virginia-based NVR Inc., one of the nation's largest homebuilders, finally landed approval on Monday for a northeast Charlotte development that was first proposed nearly two years ago. Charlotte ...

City Council approves NVR's long-delayed 150-acre community in northeast Charlotte

NVR, Inc. NVR reported mixed third-quarter 2023 results, with earnings surpassing the Zacks Consensus Estimate and Homebuilding revenues missing the same. Although the top line increased on a year-over-year basis,

NVR Q3 Earnings Beat, Homebuilding Revenues Miss, Margin Up

The company's builder and mortgage banking segments both ended up in the black, as sales for new constructions outperform the existing-home market.

NVR profits from gain on sale margins, scarce home inventory

Hikvision has recently unveiled its latest audio product line, featuring a variety of advanced network speakers. Drawing on its deep

Hikvision announces new audio product line, unveiling range of network speakers

NVR, Inc.’s NVR third-quarter 2023 earnings and homebuilding revenues are expected to have declined on a year-over-year basis. The downside is quite likely to have been driven by high mortgage rates,

NVR is Set to Report Q3 Earnings: What's in the Cards?

Homebuilders as a group were trading lower following the U.S. Census Bureau’s report that more single-family homes were built in September than in August. However, the actual number of homes built came in below views,

Housing Starts Up, Homebuilders Down as Mortgage Rates Soar

A French nun was seen tackling an environmental protester outside the construction of a new religious center in what quickly became a viral online video. On Saturday, members of the eco-group Les ...

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Additionally, decreased home construction can heighten housing shortages and drive ... The most heavily weighted homebuilding stocks are NVR, Lennar and PulteGroup. Building products is the biggest sub-industry within the S&P homebuilders index, followed ...

The Biden administration is fast-tracking construction of a wall on a 20-mile stretch of the U.S.-Mexican border in Starr County, Texas, to "deter illegal crossings in areas of high illegal entry ...

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Wondering how long it takes Robin to complete construction on the farm in Stardew Valley? Here’s an exact figure. Construction is a vital part of the farming experience in Stardew Valley.

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A Journey Through the Secrets of Firmware: From BIOS/UEFI to OS

ফার্মওয়্যারের গোপনীয়তার মাধ্যমে একটি যাত্রা: BIOS/UEFI থেকে OS পর্যন্ত

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Un viaje a través de los secretos del firmware: desde la BIOS/UEFI hasta el sistema operativo

Un voyage à travers les secrets du micrologiciel : du BIOS/UEFI au système d'exploitation

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ファームウェアの秘密を探る旅: BIOS/UEFI から OS まで

펌웨어의 비밀을 통한 여행: BIOS/UEFI에서 OS까지

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Deploying data science and machine learning applications often demands powerful computational resources. In this guide, You will learn about  the process of setting up GPU-enabled virtual servers on a Virtual Private Cloud (VPC) using Cloud services. By leveraging RAPIDS, an open-source suite of data science libraries developed by NVIDIA, you can harness the power of GPUs to accelerate your workflows. RAPIDS utilizes NVIDIA GPUs to provide high-performance data processing and machine learning capabilities, significantly reducing computation times. This post provides a step-by-step tutorial, from configuring your virtual servers to deploying RAPIDS, ensuring you have the necessary tools and knowledge to optimize your data processing tasks efficiently in a cloud environment. The GPU-enabled family of profiles provides on-demand, cost-effective access to NVIDIA GPUs. GPUs help to accelerate the processing time required for compute-intensive workloads, such as artificial intelligence (AI), machine learning, inferencing and more. To use the GPUs, you need the appropriate toolchain — such as CUDA (an acronym for Compute Unified Device Architecture) — ready. Let’s start with a simple question. What is a GPU and how is it different from CPU? Graphics processing units (GPUs) are becoming more prevalent as accelerated computing is rapidly changing the modern enterprise. They support new applications that are resource-demanding and provide new capabilities to gain valuable insights from customer data. Let’s quickly go over why you could use GPUs in the first place. By operating in a cloud environment, GPUs work in conjunction with a server’s CPU to accelerate application and processing performance. The CPU offloads compute-intensive portions of the application to the GPU, which processes large blocks of data at one time (rather than sequentially) to boost overall performance in a server environment. The following video covers the basics about GPUs, including the differences between a GPU and CPU, the top GPU applications (including industry examples) and why it’s beneficial to use GPUs on cloud infrastructure: What is RAPIDS? The RAPIDS suite of open-source software libraries and APIs give you the ability to execute end-to-end data science and analytics pipelines entirely on GPUs. RAPIDS utilizes NVIDIA CUDA primitives for low-level compute optimization and exposes GPU parallelism and high-bandwidth memory speed through user-friendly Python interfaces. Here are the features of RAPIDS as described on the NVIDIA developer website: Hassle-free integration: Accelerate your Python data-science toolchain with minimal code changes and no new tools to learn.
Top model accuracy: Increase machine-learning model accuracy by iterating on models faster and deploying them more frequently.
Reduced training time: Drastically improve your productivity with near-interactive data science.
Open source: Customizable, extensible, interoperable — the open-source software is supported by NVIDIA and built on Apache Arrow. How to provision a GPU-enabled VSI You will provision a virtual private cloud (VPC), a subnet, a security group with inbound and outbound rules and a virtual service instance (VSI) with a GPU-enabled profile using Terraform scripts with IBM Cloud Schematics. The RAPIDS, along with Docker, are configured via the VSI’s userdata field. VPC uses cloud-init technology to configure virtual server instances. The User Data field on the new virtual server for VPC page allows users to put in custom configuration options by using cloud-init. Cloud-init supports several formats for configuration data, including yaml in a cloud-config file. To understand more about Terraform and IBM Schematics, check this blog post: “ Provision Multiple Instances in a VPC Using Schematics.” In short, you can run any Terraform script just by simply pointing to the Git repository with the scripts. Note: You will need an IBM Cloud account with permissions to provision VPC resources. Check the getting started with VPC documentation. Navigate to Schematics Workspaces on IBM Cloud and click on Create workspace.


Under the Specify Template section, provide https://github.com/IBM-Cloud/vpc-tutorials/tree/master/vpc-gpu-vsi under GitHub or GitLab repository URL.


Select terraform_v0.14 as the Terraform version and click Next.


Provide the workspace name — vpc-gpu-rapids - and choose a resource group and location.


Click Next and then click Create.


You should see the Terraform variables section. Fill in the variables as per your requirement by clicking the action menu next to each of the variables: 7. Scroll to the top of the page to Generate (terraform plan) and Apply (terraform apply) the changes. 8. Click Apply plan and check the progress under the Log. (Generate plan is optional.) In the output of the log, you should see the IP_ADDRESS assigned to the VSI. Save the address for future reference. Test the setup Open a terminal. To test the setup, you need to SSH into the VSI with the below command: ssh -i ~/.ssh/<SSH_PRIVATE_KEY> root@<IP_ADDRESS> A working setup can be tested by running a base CUDA container: sudo docker run — rm — gpus all nvidia/cuda:11.0-base nvidia-smi The output should look similar to this: To check the NVIDIA driver version, run the below command: cat /proc/driver/nvidia/version Output: NVRM version: NVIDIA UNIX x86_64 Kernel Module 470.57.02 Tue Jul 13 16:14:05 UTC 2021

GCC version: gcc version 9.3.0 (Ubuntu 9.3.0–17ubuntu1~20.04) If you don’t see the expected output, check the troubleshooting section of the post. How to use RAPIDS The RAPIDS notebooks are one of the ways to explore the capabilities. These Jupyter notebooks provide examples of how to use RAPIDS. These notebooks are designed to be self-contained with the runtime version of the RAPIDS Docker Container. Run the below commands to pull a Docker image and run a container to explore RAPIDS: docker pull rapidsai/rapidsai:cuda11.0-runtime-ubuntu20.04 docker run — gpus all — rm -it -p 8888:8888 -p 8787:8787 -p 8786:8786 rapidsai/rapidsai:cuda11.0-runtime-ubuntu20.04 2. Launch a browser and enter your <IP_ADDRESS>:8888. 3. You should see the Jupyter notebook: 4. On the left pane, click on notebooks > cuml > tools and then launch the notebook. This notebook provides a simple and unified means of benchmarking single GPU cuML algorithms against their skLearn counterparts with the cuml.benchmark package in RAPIDS cuML. 5. As shown in the image above, click on the icon to launch the GPU dashboards. These dashboards provide a real-time visualization of NVIDIA GPU metrics in interactive Jupyter environments. 6. Click on GPU Resources and Machine Resources to launch the respective dashboards. 7. Run each code cell in the Jupyter notebook until you see the Nearest Neighbors — Brute Force cell. 8. Run the cell and check the metrics on the GPU resources and Machine Resources dashboard to understand the GPU utilization metrics and CPU metrics, respectively: Below the cell, you can check the CPU vs. GPU benchmark metrics. Troubleshooting To check the cloud-init logs, run the following command: cat /var/log/cloud-init-output.log If the install is configured successfully, you should see a message similar to this: _Cloud-init v. 21.2-3-g899bfaa9-0ubuntu2~20.04.1 running 'modules:final'_ _at Thu, 23 Sep 2021 08:51:11 +0000. Up 36.08 seconds._

_Cloud-init v. 21.2-3-g899bfaa9-0ubuntu2~20.04.1 finished at Thu, 23 Sep 2021 08:59:30 +0000. Datasource DataSourceNoCloud [seed=/dev/vdb][dsmode=net]. Up 534.16 seconds_ Related information RAPIDS getting started
Securely access remote instances with a bastion host If you have any queries, feel free to reach out to me on Twitter or on LinkedIn. Deploying data science and machine learning applications often demands powerful computational resources. In this guide, You will learn about  the process of setting up GPU-enabled virtual servers on a Virtual Private Cloud (VPC) using Cloud services. By leveraging RAPIDS, an open-source suite of data science libraries developed by NVIDIA, you can harness the power of GPUs to accelerate your workflows. RAPIDS utilizes NVIDIA GPUs to provide high-performance data processing and machine learning capabilities, significantly reducing computation times. This post provides a step-by-step tutorial, from configuring your virtual servers to deploying RAPIDS, ensuring you have the necessary tools and knowledge to optimize your data processing tasks efficiently in a cloud environment. The GPU-enabled family of profiles provides on-demand, cost-effective access to NVIDIA GPUs. GPUs help to accelerate the processing time required for compute-intensive workloads, such as artificial intelligence (AI) , machine learning , inferencing and more. To use the GPUs, you need the appropriate toolchain — such as CUDA (an acronym for Compute Unified Device Architecture) — ready. artificial intelligence (AI) machine learning Let’s start with a simple question. What is a GPU and how is it different from CPU? Graphics processing units (GPUs) are becoming more prevalent as accelerated computing is rapidly changing the modern enterprise. They support new applications that are resource-demanding and provide new capabilities to gain valuable insights from customer data. Let’s quickly go over why you could use GPUs in the first place. By operating in a cloud environment, GPUs work in conjunction with a server’s CPU to accelerate application and processing performance. The CPU offloads compute-intensive portions of the application to the GPU, which processes large blocks of data at one time (rather than sequentially) to boost overall performance in a server environment. The following video covers the basics about GPUs, including the differences between a GPU and CPU, the top GPU applications (including industry examples) and why it’s beneficial to use GPUs on cloud infrastructure: What is RAPIDS? The RAPIDS suite of open-source software libraries and APIs give you the ability to execute end-to-end data science and analytics pipelines entirely on GPUs. RAPIDS utilizes NVIDIA CUDA primitives for low-level compute optimization and exposes GPU parallelism and high-bandwidth memory speed through user-friendly Python interfaces. RAPIDS NVIDIA CUDA Python Here are the features of RAPIDS as described on the NVIDIA developer website: NVIDIA developer Hassle-free integration: Accelerate your Python data-science toolchain with minimal code changes and no new tools to learn. Top model accuracy: Increase machine-learning model accuracy by iterating on models faster and deploying them more frequently. Reduced training time: Drastically improve your productivity with near-interactive data science. Open source: Customizable, extensible, interoperable — the open-source software is supported by NVIDIA and built on Apache Arrow. Hassle-free integration : Accelerate your Python data-science toolchain with minimal code changes and no new tools to learn. Hassle-free integration Top model accuracy : Increase machine-learning model accuracy by iterating on models faster and deploying them more frequently. Top model accuracy machine-learning Reduced training time : Drastically improve your productivity with near-interactive data science. Reduced training time Open source : Customizable, extensible, interoperable — the open-source software is supported by NVIDIA and built on Apache Arrow. Open source How to provision a GPU-enabled VSI You will provision a virtual private cloud (VPC), a subnet, a security group with inbound and outbound rules and a virtual service instance (VSI) with a GPU-enabled profile using Terraform scripts with IBM Cloud Schematics . The RAPIDS, along with Docker, are configured via the VSI’s userdata field. IBM Cloud Schematics VPC uses cloud-init technology to configure virtual server instances. The User Data field on the new virtual server for VPC page allows users to put in custom configuration options by using cloud-init. Cloud-init supports several formats for configuration data, including yaml in a cloud-config file. User Data To understand more about Terraform and IBM Schematics, check this blog post: “ Provision Multiple Instances in a VPC Using Schematics .” In short, you can run any Terraform script just by simply pointing to the Git repository with the scripts. Provision Multiple Instances in a VPC Using Schematics Terraform Note: You will need an IBM Cloud account with permissions to provision VPC resources. Check the getting started with VPC documentation. Note: getting started with VPC Navigate to Schematics Workspaces on IBM Cloud and click on Create workspace. Under the Specify Template section, provide https://github.com/IBM-Cloud/vpc-tutorials/tree/master/vpc-gpu-vsi under GitHub or GitLab repository URL. Select terraform_v0.14 as the Terraform version and click Next. Provide the workspace name — vpc-gpu-rapids - and choose a resource group and location. Click Next and then click Create. You should see the Terraform variables section. Fill in the variables as per your requirement by clicking the action menu next to each of the variables: Navigate to Schematics Workspaces on IBM Cloud and click on Create workspace. Navigate to Schematics Workspaces on IBM Cloud and click on Create workspace. Schematics Workspaces Schematics Workspaces Create workspace. Under the Specify Template section, provide https://github.com/IBM-Cloud/vpc-tutorials/tree/master/vpc-gpu-vsi under GitHub or GitLab repository URL. Under the Specify Template section, provide https://github.com/IBM-Cloud/vpc-tutorials/tree/master/vpc-gpu-vsi under GitHub or GitLab repository URL . Specify Template https://github.com/IBM-Cloud/vpc-tutorials/tree/master/vpc-gpu-vsi GitHub or GitLab repository URL Select terraform_v0.14 as the Terraform version and click Next. Select terraform_v0.14 as the Terraform version and click Next. terraform_v0.14 Next. Provide the workspace name — vpc-gpu-rapids - and choose a resource group and location. Provide the workspace name — vpc-gpu-rapids - and choose a resource group and location. vpc-gpu-rapids Click Next and then click Create. Click Next and then click Create. Next Create. You should see the Terraform variables section. Fill in the variables as per your requirement by clicking the action menu next to each of the variables: You should see the Terraform variables section. Fill in the variables as per your requirement by clicking the action menu next to each of the variables: 7. Scroll to the top of the page to Generate (terraform plan) and Apply (terraform apply) the changes. Generate (terraform plan) Apply (terraform apply) 8. Click Apply plan and check the progress under the Log . (Generate plan is optional.) Apply plan Log In the output of the log, you should see the IP_ADDRESS assigned to the VSI. Save the address for future reference. IP_ADDRESS Test the setup Open a terminal. To test the setup, you need to SSH into the VSI with the below command: Open a terminal. To test the setup, you need to SSH into the VSI with the below command: ssh -i ~/.ssh/<SSH_PRIVATE_KEY> root@<IP_ADDRESS> ssh -i ~/.ssh/<SSH_PRIVATE_KEY> root@<IP_ADDRESS> A working setup can be tested by running a base CUDA container: A working setup can be tested by running a base CUDA container: sudo docker run — rm — gpus all nvidia/cuda:11.0-base nvidia-smi sudo docker run — rm — gpus all nvidia/cuda:11.0-base nvidia-smi The output should look similar to this: To check the NVIDIA driver version, run the below command: To check the NVIDIA driver version, run the below command: cat /proc/driver/nvidia/version cat /proc/driver/nvidia/version Output: NVRM version: NVIDIA UNIX x86_64 Kernel Module 470.57.02 Tue Jul 13 16:14:05 UTC 2021

GCC version: gcc version 9.3.0 (Ubuntu 9.3.0–17ubuntu1~20.04) NVRM version: NVIDIA UNIX x86_64 Kernel Module 470.57.02 Tue Jul 13 16:14:05 UTC 2021

GCC version: gcc version 9.3.0 (Ubuntu 9.3.0–17ubuntu1~20.04) If you don’t see the expected output, check the troubleshooting section of the post. How to use RAPIDS The RAPIDS notebooks are one of the ways to explore the capabilities. These Jupyter notebooks provide examples of how to use RAPIDS. These notebooks are designed to be self-contained with the runtime version of the RAPIDS Docker Container . RAPIDS notebooks RAPIDS Docker Container Run the below commands to pull a Docker image and run a container to explore RAPIDS: Run the below commands to pull a Docker image and run a container to explore RAPIDS: docker pull rapidsai/rapidsai:cuda11.0-runtime-ubuntu20.04 docker pull rapidsai/rapidsai:cuda11.0-runtime-ubuntu20.04 docker run — gpus all — rm -it -p 8888:8888 -p 8787:8787 -p 8786:8786 rapidsai/rapidsai:cuda11.0-runtime-ubuntu20.04 docker run — gpus all — rm -it -p 8888:8888 -p 8787:8787 -p 8786:8786 rapidsai/rapidsai:cuda11.0-runtime-ubuntu20.04 2. Launch a browser and enter your <IP_ADDRESS>:8888 . <IP_ADDRESS>:8888 3. You should see the Jupyter notebook: 4. On the left pane, click on notebooks > cuml > tools and then launch the notebook. This notebook provides a simple and unified means of benchmarking single GPU cuML algorithms against their skLearn counterparts with the cuml.benchmark package in RAPIDS cuML. notebooks cuml tools 5. As shown in the image above, click on the icon to launch the GPU dashboards. These dashboards provide a real-time visualization of NVIDIA GPU metrics in interactive Jupyter environments. GPU dashboards. 6. Click on GPU Resources and Machine Resources to launch the respective dashboards. GPU Resources Machine Resources 7. Run each code cell in the Jupyter notebook until you see the Nearest Neighbors — Brute Force cell. Nearest Neighbors — Brute Force 8. Run the cell and check the metrics on the GPU resources and Machine Resources dashboard to understand the GPU utilization metrics and CPU metrics, respectively: Below the cell, you can check the CPU vs. GPU benchmark metrics. Troubleshooting To check the cloud-init logs, run the following command: cat /var/log/cloud-init-output.log cat /var/log/cloud-init-output.log If the install is configured successfully, you should see a message similar to this: _Cloud-init v. 21.2-3-g899bfaa9-0ubuntu2~20.04.1 running 'modules:final'_ _at Thu, 23 Sep 2021 08:51:11 +0000. Up 36.08 seconds._

_Cloud-init v. 21.2-3-g899bfaa9-0ubuntu2~20.04.1 finished at Thu, 23 Sep 2021 08:59:30 +0000. Datasource DataSourceNoCloud [seed=/dev/vdb][dsmode=net]. Up 534.16 seconds_ _Cloud-init v. 21.2-3-g899bfaa9-0ubuntu2~20.04.1 running 'modules:final'_ _at Thu, 23 Sep 2021 08:51:11 +0000. Up 36.08 seconds._

_Cloud-init v. 21.2-3-g899bfaa9-0ubuntu2~20.04.1 finished at Thu, 23 Sep 2021 08:59:30 +0000. Datasource DataSourceNoCloud [seed=/dev/vdb][dsmode=net]. Up 534.16 seconds_ Related information RAPIDS getting started Securely access remote instances with a bastion host RAPIDS getting started RAPIDS getting started Securely access remote instances with a bastion host Securely access remote instances with a bastion host If you have any queries, feel free to reach out to me on Twitter or on LinkedIn. If you have any queries, feel free to reach out to me on Twitter or on LinkedIn . Twitter LinkedIn

How to Deploy RAPIDs on GPU-Enabled Private Cloud

Since the introduction of the 15-inch MacBook Pro in October 2016, the Touch Bar has generated controversy. Some critics have called it a gimmick due to frequent freezes and functional concerns, such as certain keys not responding or failing to display shortcut buttons. The Touch Bar on the MacBook Pro provides a terrific method to swiftly access system-wide and app-specific functionality. It's a unique feature on MacBook Pro models that enables you to contact Siri, change settings, and also offers a number of shortcuts for each program by using actions such as swiping, sliding, or tapping. However, despite being a really helpful innovation, the Touch Bar occasionally malfunctions and prevents you from using those crucial shortcuts. If you've been having this issue, be rest assured that you can fix it. This article will walk you through each step of fixing your MacBook Touch Bar to get it working at its best but first, let’s understand why your Mac’s Touch Bar might be malfunctioning. Why Your MacBook Pro Touch Bar Is Not Working? Understanding what might be causing your Mac's Touch Bar to malfunction is the first step towards fixing it. You need to understand why the Touch Bar freezes suddenly, is unresponsive or does not display application-specific buttons. There are various explanations to this but some of the most common ones are: Apps malfunctioning Low memory, storage and CPU space Water spillage on Mac Old Mac operating system Hardware issues Software errors 7 Ways To Fix Your MacBook Pro Touch Bar Below is a step-by-step guide to fix the Touch Bar on a Mac. 1. Force Quit Any Application Causing Issues If you open or switch to a specific application and then notice that the Mac Touch Bar isn't responding or working properly, the app is probably stuck. If that's the problem, you'll need to forcibly close that program. Here's how to force the problematic program to close in order to resolve Touch Bar issues: To open the Force Quit Dialogue, use Command + Option + Esc. Select the app causing Touch ID malfunction and click Force Quit. Here is an alternative method: Locate the Apple menu on the left upper corner of your Mac screen Select Force Quit Select the application that is not responding. Then click the Force Quit button 2. Restart Your MacBook A simple restart of your MacBook device will often be sufficient to solve the problem of the Mac Touch bar not responding. These are simple steps to follow in order to restart your MacBook: Click on the Apple Menu on your MacBook screen. Select Restart. Click on the Restart button in the box to complete your action. In a situation whereby your MacBook screen freezes, all you need to do is to press and hold down the power button to forcibly shut down the MacBook. After some time, turn on your MacBook. You need to save all of your work before restarting your Mac so as not to lose them. 3. Refresh MacBook Pro Touch Bar Another effective way to fix MacBook Pro Touch Bar not functioning well is to refresh. It is an effective process because it will clean every resource and memory it is currently using. It will then assign new resources to the Mac Touch Bar after the refresh process is completed. This refreshing procedure is helpful when your Mac’s Touch Bar backlight freezes or when you are experiencing disappearing issues. There are two ways you can refresh. The Terminal or the Activity Monitor method. How to Refresh Mac’s Touch Bar Using Terminal Search for the terminal app with spotlight search or go to ‘Finder’. Click on Applications and then select utilities folder. Once the terminal app is opened, enter the command: 'sudo pkill TouchBarServer' Select Enter To finish the process, terminal requires your administrator password to refresh your Mac Touch Bar. While you type in your password, it might not show any characters. It's normal. Just press the return key again. The system will then immediately refresh the process that controls the Mac Touch Bar and it will function as expected. How to Refresh Mac’s Touch Bar Using Activity Monitor Another way you can refresh your Mac’s Touch Bar process is via Activity Monitor. It shows all of the processes that are currently running on your MacBook. Below is a step-by-step guide on how to go about it: Go to Finder and search for ‘Activity’. Select and click on Activity Monitor Once you find the Activity Monitor, search for Control Strip and select it As you select, click on the x icon at the upper end of the Activity Monitor window. Click on Quit to force shut down the list of processes that are running and then your Touch Bar is back to functioning properly. 4. Refresh Control Strip In a situation where the Mac Touch Bar is not responding to shortcuts but rather showing a blank opening, reloading the control strip is the best option to fix the issue. The control strip is located on the right side of the Mac’s Touch Bar. It contains buttons for volume, adjusting the brightness as well as ‘Ask Siri’. So if you are having a problem with Touch Bar volume or your Touch Bar brightness is not accessible, you just need to refresh the control strip. How to Refresh Control Strip on Mac Go to Finder, click on Applications and the select Utilities In the Utilities folder, click on Terminal Once you get to the Terminal app, enter the following command: ‘killall controlstrip’ and press the return button. It will refresh the Mac’s Touch Bar and automatically fix any issue related to the Touch Bar. 5. Update MacOS to The Latest Version MacOS constantly releases updates that fix issues and enhance support for internal features like the touch bar. A guaranteed technique to rule out outdated software as a potential reason your touch bar isn't working is to make sure you have the most recent releases from Apple. Go to Software Update under System Preferences. Install it if one is available. 6. Reset Your Mac’s SMC Your Mac's SMC, or System Management Controller, is in charge of overseeing a variety of different operations. If restarting your MacBook didn't help, you might try resetting the SMC on your Mac. Note that this advise does not apply to Macs with M1 chips because they lack SMCs. Here's how to restart your Mac's SMC: Turn off your MacBook. Hold down Control, Option, and Shift on the built-in keyboards for seven seconds. Holding these three keys for 7 seconds will also require you to press and hold the power button. So, you have four keys pressed at once. Hold all four keys for a further seven seconds, then release them. Press the power button to turn on your MacBook after a brief delay. 7. Reset Your Mac’s NVRAM/PRAM Restarting your Mac's NVRAM and PRAM is a more involved version of restarting your MacBook, much like restarting the SMC is. This has a decent chance to succeed if nothing else has succeeded so far. Here's how to do it: Shut down your Mac. Turn it on, press and hold Option, Command, P, and R together. After around 20 seconds, during which your Mac will restart, you can release the keys. You can let off of the keys on Mac computers that play a starting sound after the second sound. After the Apple logo shows and disappears for a second time on Mac machines with the Apple T2 Security Chip, you can release the keys. You might wish to access System Preferences after your Mac has finished booting up and change any reset settings, like the speaker volume, display resolution, initial disk option, or time zone. Conclusion If you've exhausted all possible options, you might need to schedule a meeting with Apple Support. You're probably dealing with a hardware problem or a unique issue, which an Apple specialist can help you identify more accurately.

Everything You Need to Know About Fixing The MacBook Pro Touch Bar

Crypto Crash? Not Really... 

belong to the semiconductor memory. ROM is the abbreviation of read-only memory, and RAM is the abbreviation of random access memory. ROM can keep data when the system powers off, and RAM is the opposite, a typical RAM is the computer memory. But sometimes we are not clear: what is the difference between Flash Memory and ROM and RAM? How much do you really know about memory, or RAM, ROM, and flash memory specifically? Here we will tell them in different aspects, this article will act as a guide to basic memories. ROM and RAM 1. What are the types of RAM? RAM Upgrade Guide - What You Need to Know 1.1 Static RAM (SRAM) SRAM is very fast and the fastest storage device to read and write currently, but it is also very expensive, so it is only used in demanding places, such as the first and second level buffer of CPU. 1.2 Dynamic RAM (DRAM) DRAM keeps data for a short time and its operation is slower than the SRAM, but it's still faster than the ROM. In addition, it's much cheaper than SRAM. The computer memory is DRAM. DRAM is divided into many kinds, the most common ones are FPRAM / FastPage, EDORAM, SDRAM, DDR, RAM, RDRAM, SGRAM, WRAM, and so on. DDR RAM, one of which is introduced here. DDR RAM (Double-Date-Rate RAM), also known as DDR SDRAM, the improved RAM, and SDRAM, are essentially the same, except that they can read and write data twice in one clock, doubling the speed of data transmission. This is the most used memory in a computer today, and it has a cost advantage, which in fact defeats another Intel memory~ Rambus DRAM. Many high-end graphics cards are also equipped with high-speed DDR RAM to increase bandwidth, which can greatly improve the pixel rendering capability of 3D accelerator cards. 2. What are the types of ROM? PROM (programmable ROM), is programmed once that can not be modified, this is an early product, now it is gone. EPROM (erasable and programmable ROM), is a kind of universal memory that erases the original program by ultraviolet radiation. EEPROM is electronically erased, very expensive, long writing time. For example, mobile phone software is generally placed in the EEPROM, when we call, some of the last numbers that are dialed are temporarily in the SRAM, but not immediately written in the call log (the call record is stored in the EEPROM). Memory is used to store data and programs that are currently in use (that is in execution), and the memory of the computer we normally used refers to dynamic memory (that is DRAM). The so-called "dynamic" in dynamic memory refers to when we write data to the DRAM, after a period of time, the data will be lost, so we need an extra circuit to refresh the memory. It works like this: whether a DRAM's memory cell stores 0 or 1 depends on whether the capacitor has a charge. 1 represents a charge, 0 represents no charge. But over time, the capacitance of 1 will discharge, and the capacitance of 0 will absorb the charge, which is why the data is lost. Taking refresh operation regularly checks the capacitor, if the charge is greater than 1/2 of the full charge, it is considered to represent 1, charging the capacitor fully; If the quantity of electricity is less than 1/2, it is considered to represent 0, and the capacitor is discharged to maintain the continuity of the data. 3. What is Flash Memory? Flash memory combines the advantages of ROM and RAM. It not only has the electrically erasable and programmable capability (EEPROM), but also can read data quickly without power loss (the advantage of NVRAM). This memory is used in USB and MP3. For the past 20 years, embedded systems have been using ROM (EPROM) as their storage device. However, in recent years, FLASH has completely replaced the position of ROM (EPROM) in embedded systems to store Bootloader and operating systems, or Program code or directly as the hard disk (U disk). At present, there are two kinds of NOR Flash and NAND Flash. NOR Flash reading is the same as the common SDRAM reading. Users can run the code loaded in NOR FLASH directly, which reduces the capacity of the SRAM and saves cost. NAND Flash doesn’t have a memory random read technique, which reads in the form of a block at a time, usually 512 bytes at a time, thus it is cheaper. Users cannot run program code on NAND directly, so many developers using NAND have to use a small NOR Flash to startup and run code. NOR Flash is generally used in small capacity because of its fast reading speed, and it is used to store important information such as operating systems. The most common NAND FLASH used for large capacity combines with embedded system DOC (Disk On Chip) and common "flash disk", which can be erased online. At present, the FLASH on the market mainly comes from Intel, AMD, Fujitsu, and Toshiba, and the main manufacturers of NAND Flash are Samsung and Toshiba. NOR and NAND are the two main non-volatile flash memory technologies in the market. Intel first developed NOR flash technology in 1988, which completely changed the situation in which EPROM and EEPROM dominated the world. Then, in 1989, Toshiba published its NAND flash architecture, emphasizing lower cost per bit, higher performance, and easy upgrades via interfaces like disks. But after more than a decade, a considerable number of hardware engineers still can not distinguish between NOR and NAND flash memory. Flash memory is often used interchangeably with phase NOR memory. Many in the industry are also confused about the advantages of NAND flash over NOR because in most cases flash memory is only used to store a small amount of code, and NOR flash memory may be more appropriate, while NAND is an ideal solution for data needed high storage density. NOR is the main non-volatile flash memory technology in the market. NOR is generally used to store a small amount of code, especially in code storage media. NOR is characterized by simple application, no special interface circuit, high transmission efficiency, it belongs to the in-chip execution(XIP, eXecute In Place), so that applications can run directly in (NOR) flash memory. No longer need to read the code into the system RAM. Low write and erase speed can be very cost-effective but affect its performance for a small capacity when it is 1~4MB. NOR flash comes with an SRAM interface and has enough address pins to access, it is convenient to store and use each byte. NOR flash accounts for the majority of the 1~16MB flash market. NAND structure can provide extremely high cell density and high storage density, and fast write and erase speed. The difficulty of applying NAND lies in the management of flash and the need for a special system interface. 3.1 Performance Comparison Flash memory is non-volatile memory and can be erased and reprogrammed on the memory cell block. The write operation of any flash device can only be done in an empty or erased cell, so in most cases, the erasure must be performed before the writing. In general, the NAND devices perform the erasure operation easily. But NOR requires that all bits in the target block be written as 1 before erasing. Since the NOR device is erased as a block of 64~128KB, the time to perform a write / erase operation is 5s. In contrast, the erasure NAND device is performed as a block of 8~32KB, and the same operation only needs 4 ms at most. The performance gap between NOR and NADN is further widened by the difference of block size in the execution of erasure. It is shown that for a given set of write operations (especially when updating small files), more erasure operations must be performed in NOR-based cells. Therefore, when choosing a storage solution, the designer must weigh the following factors: The read speed of NOR is a little faster than NAND, but the writing speed is the opposite. The 4ms erasure speed of NAND is much faster than that of NOR. Most writing operations need running erasure. The NAND erasure unit is smaller and the corresponding erasure circuit is less. 3.2 Interface Difference NOR flash has a SRAM interface, and enough address pins to address, and easy storage and program to every byte inside. NAND devices use complex I/O ports to serially access data, which may vary from product to product or from manufacturer to manufacturer. Eight pins are used to transmit and control address and data information. NAND read and write operations use 512-byte blocks, which is similar to hard disk management. Naturally, NAND-based memory can replace hard drives or other block devices. 3.3 Capacity and Cost The cell size of NAND flash is almost half that of NOR devices. Because of the simpler manufacturing process, the NAND structure can provide higher capacity within a given die size, thus lowering the price accordingly. NOR flash accounts for most of the 1~16MB flash market, while NAND flash is only used in 8~128MB products, which means that NOR is mainly used in code storage, and NAND is suitable for data storage, mainly used in CompactFlash, Secure Digital, PC Cards, and MMC memory cards. 3.4 Reliability and Durability One of the key issues needed to consider is reliability when using flashs media. For systems that need to extend MTBF, Flash is a very suitable storage solution. So the reliability of NOR and NAND can be compared between bit switching, lifetime (durability), and bad block processing. Service Life The maximum number of erasures per block in NAND flash memory is one million times, while that of NOR is 100,000 times. In addition, NAND memory has the advantage of 10: 1 erasure speed, 8 times smaller than that of the NOR device, and each NAND memory block has fewer deletions at a given time. Bit Switching All-flash devices are plagued by bit switching. In some cases (rarely, NAND occurs more often than NOR), a bit reverses, or its reversion is reported. One changing bit may not be obvious, but if it happens on a critical file, this minor failure could cause the system to shut down. If the existing wrong report, it may be solved by reading it a few more times. Of course, if the bit really changes, the error detection/error correction (EDC/ECC) algorithm must be used. The problem of bit inversion is more common in NAND flash memory, therefore, it is recommended that using NAND flash memory and EDC/ECC algorithm at the same time. But this problem is not fatal when storing multimedia information in NAND. Of course, if you use a local storage device to store an operating system, configuration file, or other sensitive information, you must use the EDC/ECC system to ensure reliability. Bad Block Processing The bad blocks in NAND devices are randomly distributed. There have been previous efforts to eliminate bad blocks, but they found that the yield is too low and the cost is too high. The NAND device needs to initialize the medium to detect the bad block and mark it. In the fabricated devices, the failure rate will be high if this treatment cannot be carried out by a reliable method. 3.5 Simple Operation NOR-based flash memory can be used very directly, can be connected to other memory, and can run code directly on it. NAND is much more complex because of the need for an I / O interface. Access methods for various NAND devices vary from manufacturer to manufacturer. When using NAND devices, you must write to the driver before doing anything else. Writing information to NAND devices requires that designers don’t write to bad blocks, which means virtual mapping is necessary all the time on NAND devices. 3.6 Software Support When discussing software support, a distinction should be made between basic read/write / erasing operations and higher-level software for disk emulation and algorithms of flash management, also including performance optimization. Running code on a NOR device doesn't require any software support. But using a NAND device, you usually need a driver, that is MTD(Memory Technology Driver). Both NAND and NOR devices need MTD when writing and erasing operations. NOR devices require less MTD, because many vendors offer more advanced software for NOR devices, including M-System 's TrueFFS driver, which is used by manufacturers such as Wind River System, Microsoft, QNX Software System, Symbian, and Intel. It also used to simulate DiskOnChip products and manage the NAND flash memory, including error correction, bad block handling, and loss balancing. The main manufacturers of NOR FLASH are INTEL and MICRO, which used to be the mainstream of FLASH products, but its market share now squeezed by NAND FLASH. Its advantage is that it can run programs directly from FLASH, but the process is complex, thus it is expensive. NAND FLASH's main suppliers are SAMSUNG and Toshiba, and it widely used in USB drives, memory cards, and MP3 players. With different processes, it has a larger storage capacity and is cheaper than the NOR FLASH. But also has the drawback, is unable to address runs the program directly, only used to store the data. In addition, NAND FLASH is very prone to bad blocks, so it is necessary to detect it by the related algorithm. NAND FLASH is used to store data and programs in laptops, but it must be started with NOR FLASH. In addition to SAMSUNG processors, other mainstream processors for laptops do not support NAND FLASH startup directly. Therefore, you must start the machine with a small piece of NOR FLASH, and run OS and other software through the NAND FLASH load into SDRAM. 4. Conclusion The DRAM uses the charge on the gate capacitor of the MOS transistor to store the information. Once the power is down, all the information will be lost. Because the grid will leak, it needs refresh operation to replenish the charge on these gate capacitors regularly. And every time the data is read out, it requires running the same work. This is called dynamic refresh, so it is called dynamic RAM. Because it uses only one MOS to store information, it can be highly integrated and can do a lot of work. SRAM uses registers to store information, so once the power is down, the data will be lost. But when the power supply, its data will always exist without the need for a dynamic refresh, thus it is called static RAM. The above is mainly used in the system with large capacity, does not need to recover data through refresh operation. Flash ROM uses the capacitor on the floating gate to store the charge to store the information, because the floating gate does not leak, so the information can still be saved after the power is off. Also because of its simple mechanism, it can be integrated highly, and the capacity can be very large. Flash ROM needs to be erased by electricity before writing, but in a different approach of erasure, EEPROM can be erased in bytes, and flash ROM can only be done in the sector. However, it can be written in byte units, mainly used for bios, U disk, and Mp3 devices that require large capacity and data storage when power is down. FAQ 1.What is ROM and its function? Read-only memory (ROM) is a type of storage medium that permanently stores data on personal computers (PCs) and other electronic devices. It contains the programming needed to start a PC, which is essential for boot-up; it performs major input/output tasks and holds programs or software instructions. 2. What are the four types of ROM? MROM (Masked ROM) ... PROM (Programmable Read-Only Memory) ... EPROM (Erasable and Programmable Read-Only Memory) ... EEPROM (Electrically Erasable and Programmable Read-Only Memory) ... 3. What is a RAM in a computer? RAM stands for random-access memory, but what does that mean? Your computer RAM is essentially short-term memory where data is stored as the processor needs it. This isn't to be confused with long-term data that's stored on your hard drive, which stays there even when your computer is turned off. 4. What is flash memory used for? Flash memory is a long-life and non-volatile storage chip that is widely used in embedded systems. It can keep stored data and information even when the power is off. It can be electrically erased and reprogrammed. Flash memory was developed from EEPROM (electronically erasable programmable read-only memory). 5. Is Flash memory a RAM or ROM? RAM is Read-Only Memory. Unlike RAM, ROM is persistent storage. ... Flash Memory is one category of ROM i.e Electrically Erasable Read Only Memory (EEPROM). 6. What is flash memory and its types? Flash memory is a type of erasable read-only memory (EEPROM) that clears and rewrites data in chunks for fast, energy-efficient access and rewriting. Flash memory, or flash storage, is non-volatile, which means it remains viable even without an active power source. Previously published at https://www.kynix.com/Blog/Tutorial-The-Difference-Between-Various-Memories.html

Differences Between RAM, ROM, And Flash Memory: All You Need To Know

Diferencias entre RAM, ROM y memoria flash: todo lo que necesita saber

Most consumer radio products (think 802.11b/g/n, a.k.a WiFi, Bluetooth, Zigbee, Thread...) nowadays somehow make use of the ISM band around 2.4GHz. This band was chosen by multiple technologies for a series of reasons, the prominent one being that it's unlicensed. Its success, however, brought with it a key problem as well: overcrowding and interference. While most of us have experienced inter-device interference (ever scanned for WiFi and got back a list of 666 networks in range?), a more subtle form is what you could call intra-device interference, i.e. that experienced by devices that host more than one radio. The typical example in consumer electronics would be devices that support both WiFi and Bluetooth, but more esoteric combinations exist. The problem is simple to understand: let's say you have a WiFi radio with its antenna and a Bluetooth radio right next to it with its own antenna. They don't know about each other, so there is a high chance that they might transmit at the same time (the higher their combined duty cycle, the higher the chance). If they do, and if they happen to be transmitting close in the frequency domain, their transmissions will interfere with each other. Mitigation strategies Interference is usually solved by multiplexing over whichever domain possible. Physical separation Physical domain multiplexing would be a fancy expression for "keeping the antennas far from each other", which is one of the best practices in designing these systems. However, this approach is usually limited by the size of the product you're designing. If the PCB is a few centimeters long, that's as far as you can place antennas. Frequency separation Frequency domain multiplexing can potentially help a lot more. A good example of this is channel management in 802.11b/g/n: the 2.4GHz spectrum is divided in up to 14 channels (or as few as 11 in certain countries) each with a bandwidth of 22 MHz and a channel separation of 5 MHz, and WiFi access points select the channel that looks the least busy. Bluetooth uses a more dynamic approach called frequency hopping, in which the spectrum is split into a high number of channels and the radios jump from one to another following a pseudo-random pattern. This makes it less likely that a single, strong source of interference can disrupt a connection; however, a "uniformly" noisy 2.4GHz environment, or a very  close source with strong harmonics, will still be an issue. 802.15.4 (the standard on which Zigbee and Thread are based) uses 16 channels, (unintuitively) numbered from 11 to 26, with a bandwidth of 2 MHz and a channel separation of 5 MHz. Channel management is similar to WiFi, with the whole network being on the same channel and no frequency hopping. Channel changes are allowed, but a policy on when and how to do that is not standardised. Packet Traffic Arbitration as a solution PTA (Packet Traffic Arbitration) tries to help with time domain multiplexing. What that means is that one of the radios can act as a master, the other as a slave, and the master will decide (arbitrate) access to the air medium to avoid that the radios transmit (or, to a degree, expect to receive) something at the same time. PTA is described in IEEE 802.15.2 (2003) Clause 6. However, PTA is a recommendation, not a standard, so implementations vary in the details. The most common form of it uses 3 signals, usually called REQUEST, PRIORITY and GRANT. The slave can assert REQUEST to signal that it needs to access the air medium. Optionally, it can assert PRIORITY to influence the master's arbitration in its own favour. The master is responsible of asserting GRANT in response when it sees fit. This is where implementations diverge in the details of how this decision is taken. Some keep GRANT asserted whenever they are not transmitting, even if REQUEST is not asserted. Others will GRANT immediately on REQUEST if the medium is free, otherwise complete their transmission and then GRANT. However the GRANT happens, once it is asserted the slave will transmit and de-assert REQUEST when the medium is no longer required. This is also implementation dependent, as some slaves will de-assert immediately after transmitting while others, expecting an immediate reply, will wait some time before releasing the medium. There are also 4 wire (using an extra FREQ signal), 2 wire (omitting PRIORITY) and 1 wire (using just GRANT) variations, which are however less common. In any case, the master role is usually assigned to the WiFi radio in the system, as WiFi is usually the technology with the highest duty cycle among the ones involved. Getting practical with the CYW43143 and EFR32 For a series of reasons, PTA has not been used much recently. The most frequent use case (certainly the one that shifts the biggest amount of ICs) would be Bluetooth + WiFi in laptops and mobile phones, which is better served by so called combo chips. These integrate 2.4GHz WiFi, Bluetooth and 5GHz WiFi in one package, eliminating the need for PTA traces between 2 chips while also saving on BoM cost and PCB surface, which are both at a premium on mass marketed, portable devices. However, at , in a recent design we needed to do PTA at all costs. The system had the CYW43143 WiFi 2.4GHz radio (originally by Broadcom, then acquired by Cypress, in turn acquired by Infineon). The other 2.4GHz technology involved was Thread, a mesh network stack based on 802.15.4 (same as Zigbee) and IPv6. This of course ruled out a wifi-bluetooth combo chip. Our radio choice was a Silicon Labs EFR32. Elimo Engineering The board was based on an Allwinner A13 SoC running a heavily customised build of OpenWrt.The board already followed best practices to separate the antennas and frequency separation does not help much (Thread is less frequency-agile than WiFi, in which Access Points can move channel at any point in time); as a result, interference did occur frequently and PTA came to the rescue. Kind of. Enabling PTA The EFR32 played nice; Silicon Labs provides several libraries (called plugins) with its Thread stack, and one of them is a PTA library, which can be configured to use up to 3 wires, with different polarities, different request logics etc. The only significant limitation is that it only implements slave logic, but that was not a problem in our case (the 43143 only implements a master, so they fit perfectly). After a bit of configuration and a rebuild, the EFR32 was using REQUEST and PRIORITY as expected, as verified with a logic analyser (via the trusty !). sigrok First trouble As you can see from the GRANT line trace above, though, something was wrong on the other side. Despite running a ping at 1Hz on the Thread network, the (active low) GRANT line being stuck low meant that the 43143 was essentially not doing any arbitration and always giving the go ahead. This was the result of ignorance and (wrongly) assuming that the functionality would be enabled either by the driver that we used, or by the firmware that is loaded to the 43143 when the module is loaded. Here begins the trouble. brcmfmac brcmfmac The Cypress application note about coexistence ( ) covers both the proprietary, Cypress-only SECI interface (which can only be used between Cypress chips) and the "standard" PTA interface, and for the latter it mentions that certain NVRAM parameters need to be set. AN214852 USB - SDIO disagreements So far so good, except that setting NVRAM parameters is not as straightforward as you might hope. A look at the kernel module code shows that for SDIO chips, the module will load a firmware blob from and an file with NVRAM parameters. Easy peasy. brcmfmac /lib/firmware/brcm nvram.txt In our system, however, we use a USB interface to the 43143, and unfortunately the approach the module takes for USB chips is different: only a firmware blob is loaded, no trace of an nvram file. So, PTA requires custom NVRAM, the IC is USB and the USB code does not use an NVRAM file: kind of a nasty roadblock, since I (again, wrongly) thought that modifying the blob was a no-go. brcmfmac Compare the code from (note argument 1 is 0 and argument 3 is NULL, meaning nvram file is not loaded) ... brcmfmac/usb.c ret = brcmf_fw_get_firmwares(dev, , brcmf_usb_get_fwname(devinfo), , brcmf_usb_probe_phase2); (ret) {
brcmf_err( , ret); fail;
} /* request firmware here */ 0 NULL if "firmware request failed: %d\n" goto ... to the one from (note argument 1 is BRCMF_FW_REQUEST_NVRAM, argument 4 is not null): brcmfmac/sdio.c ret = brcmf_fw_get_firmwares(sdiodev->dev, BRCMF_FW_REQUEST_NVRAM, sdiodev->fw_name, sdiodev->nvram_name, brcmf_sdio_firmware_callback); OpenWrt to the rescue Thanks to the help of a couple of folks (PaulFertser and rmilecki) from the IRC channel on freenode, however, I had a couple of lucky breaks. #openwrt-devel First, the blob that is loaded for USB devices is a fairly standard TRX binary format. $ ./otrx check brcmfmac43143.bin
Found a valid TRX version Second, the blob contains the nvram parameters as zero terminated ASCII strings, and they are all just appended at the end. $ strings brcmfmac43143.bin

[SNIP]

extpagain2g=0
pdetrange2g=0
triso2g=3
antswctl2g=0
maxp2ga0=82
mcsbw202gpo=0x75333333
mcsbw402gpo=0x97544444
legofdmbw202gpo=0x75310000
cckbw202gpo=0x1111
swctrlmap_2g=0x0a050a05,0x0a050a05,0x00000000,0x00000000,0x000
xtalfreq=20000
otpimagesize=154
tempthresh=120
temps_period=5
temp_hysteresis=5
rssismf2g=0x8
rssismc2g=0x8
rssisav2g=0x2
loopbw2g=100
txalpfpu=1
aci_detect_en_2g=1
rxgaintempcoeff2g=60
$ Together with , a TRX format editor/checker written by rmilecki, this allowed me to use an hex editor to alter the blob and add the parameters from AN214852. otrx Easy, right? Nah. Deciphering the scripture Unfortunately, there is no documentation about said parameters outside of AN214852 and, even there, you are only given an example for a "4343W board" without explaining how to adapt them to other boards/ICs or what they do specifically. Of course the example parameters for the 4343W did not work on the 43143, so some guesswork was needed. As you can see in the logic trace above, using the values in the AN, the GRANT line was being toggled once in a while, and it was no longer stuck low. A sign that PTA was enabled, but something more was wrong. In fact, you can also see that the GRANT line was only sporadically toggling. Time to dig into the meaning of coexistence parameters from the AN: boardflags= zbcxpadnum= zbcxfnsel= zbcxgcigpio= 0x1 0x040201 0x233 0x123 and a few lines below, this slightly confusing addition: Boardflags= Boardflags2= 0x00000001 /* SECI coex */ 0x00000080 /* Board support legacy 3/4 wire */ This is accompanied by the following parameter bit maps: So, the general idea was that and would enable coexistence, and they should be set to and to do so. boardflags boardflags2 0x01 0x80 was reasonably simple: a the bitmap makes it obviously about which GPIO is used by which signal. On our board, GRANT is on GPIO1, PRIORITY on GPIO2 and REQUEST on GPIO4, so this needed to be changed to . zbcxpadnum 0x010204 is kind of a mystery still: the AN only mentions GCI, which seems to stand for Global Coexistence Interface, but says nothing more about it. From a quick Google search it seems GCI is a serial protocol, so this is probably not related to us. However, not defining this parameter seemed to go back to GRANT being always low. For lack of better understanding, I set it to the example value of . zbcxgcigpio 0x123 The key problem however was hiding behind , which controls, it seems, the muxing of different functions to different pins. The example of is not correct for the 43143, on which the PTA functions seem to be on position 3 for all GPIOs, probably giving us a value of for . This is a guess, since as you can see in the screenshot below, the 43143 datasheet does not specify the value. However, the "Legacy BT coexistence" function is always listed in 3rd place, so I decided to give it a go. zbcxfnsel 0x233 0x333 zbcxfnsel Taking it for a spin Let's add the parameters we decided above to the binary (see the last two lines): Now, TRX has a CRC, so these changes make the CRC wrong; luckily, prints out both the expected and the computed CRC: otrx $ ./otrx check brcmfmac43143.bin
Invalid data crc32: 0x4dc00fe3 instead of 0x2a665d76 Let's fix the CRC (taking into account the binary endianness, we need to reverse the CRC bytes) Now, let's place this new binary in and do a cold reboot (because will skip downloading the firmware if one is running already) to let the kernel module load the new binary. /lib/firmware/brcm/ brcmfmac A test with running on the wifi interface while pinging a host on the Thread network shows the following: iperf3 As hoped, the 43143 is now a PTA master and the EFR32 correctly honours the GRANT signal.

Reverse Engineering Broadcom Chips To Enable Packet Traffic Arbitration for 2.4GHz Co-Existence

Use Beta Distribution and Thompson Sampling to Beat The Multi-armed Bandit at the Casino

Up and Running with Ubuntu, Nvidia, Cuda, CuDNN, TensorFlow, and Pytorch

September 12th event was all about the new iPhones. Despite this, introduced a few other major updates in their product line. One of them was Apple Watch Series 3 with cellular support. Apple Having a watch, a phone and an iPod with unlimited songs on your wrist is really impressive, however one important detail in particular slipped from most of the reviews. Apple Watch Series 3 can become the driver of electronic SIM (eSIM) in telecom industry. One of the first eSIM devices — Samsung Gear S3, even after almost a year on the market, . While Apple announced with more countries to follow in 2018. This is a start of eSIM era in mobile. I have spent 9 years developing for SIM cards and I can be biased but I truly believe — that is something really remarkable. supports LTE only in US, Korea and Singapore LTE support for Apple Watch Series 3 in 10 countries software Intro to SIM Disclosure: this chapter has a lot of technical details. If you wonder about the future of SIM-less iPhones — just skip to the next one. So, what SIM card is exactly and why dowe still have them, taking space in our iPhones, while Apple is removing mini-jack to increase space inside our iPhones? A lot of people think about SIM as just a simple memory card, like microSD, with your subscription data. However it is a tiny and a very secure computer with CPU, RAM, optional ROM, NVRAM (like hard drive in computers). It has OS, filesystem and even applications often run on (drumroll…) Java! (well, javacard, to be completely honest, but it is still a subset of Java). Here is a typical SIM-card architecture: Your phone subscription consist of several important parts. Pair of IMSI and Ki — id and a secret key used for identification and authentication in the network. This data is stored during card personalisation on the factory and only your operator’s Authentication Centre (AuC) knows this key. OTA (Over The Air) keys. OTA — is the way for the operator to update data on the SIM card remotely using a sequence of encrypted SMS messages. OTA is optional but very common nowadays. There is a number of network related files too, with settings such as which networks have priorities in what countries and which networks are blocked, what iSMS gateway should be used, and so on. Basically a lot of settings files. All those components called should reside on a very secure chip. Modern SIM cards even have protections against attacks that use electronic microscopes to read their content. Electronic Profile Below is an example of network authentication and session generation key procedure. - random sequence generated on the operator’s side and - crypto algorithms used for authentication and session key generation respectively - session key (signed response) - authentication response RAND A8 A3 Kc SRES The Future of SIM cards Telecom standards industry is a very slow in general. For example MicroSIM (3FF) standard was and ! It took another 7 years for this standard to be adopted by Apple in its first iPad. eSIM standard but it is not something that is currently used in mass market. First mass market device with eSIM support, Samsung Gear S3, was launched in late 2016. And Apple Watch can promote this standard among operators. proposed in 1998 standardised in 2003 was introduced in 2013 To install profile to Apple Watch eSIM user will need to use an iPhone. iPhone will download network operator’s profile in encrypted form, cut it into small chunks and feed to the Apple Watch. Apple Watch will store this profile in secure part of the modem chip. It seems like very soon (very likely next year) we will see an iPhone with eSIM. We will be able to change our network operator via App Store or a special app from Apple. Yes, there is a possibility that Apple will introduce “Mobile Plans” in the list of its App Store categories! This can fundamentally change the way we use mobile plans. You will no longer need to go to a mobile operator shop to subscribe to or to change your mobile plan. Few taps in the app and the new SIM is ready to use on your phone. According new version of your phone will be able to store more than one mobile plan and provide an easy way to switch between plans. This can be especially handy if you travel abroad. Remote SIM Provisioning specification In fairness, there are a lot of Android smartphones around with hardware support for eSIM already. Technically XMMTM 7260 and XMM 7360 modems from Intel and Qualcomm Snapdragon 835 are able to host an eSIM. In order to install eSIM profile smartphone should have Local Profile Assistant (LPA) app. In iPhone an LPA will be app from Apple. Apple doesn’t provide developers access to SIM card and there is no chance they will do that in future. But a lot of Android devices have an API to send commands to their SIM card. This means that LPA for Android phones could be developed by mobile operators in the near future. There is no extra work requred from Google required to start that process. The question here is: who will launch the first fully functional eSIM in a smarphone faster — Apple in next iPhone or various mobile operators on Android.

Stories, News & Mentions about NVR

Nvr’s Stories on HackerNoon

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