BRN Discussion Ongoing
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Space Cadet
Regular
Really Good effort. Well done!!!! I had been wondering why we weren’t on SiFive’s website. Now I don’t have to wonder anymore.View attachment 17025
I noticed that the brainchip was not listed as Partners on SiFive's website and notified them yesterday.
They've already fixed it and confirmed back to me just then.
Bravo
If ARM was an arm, BRN would be its biceps💪!
Howdy All,
I was doing some more research on SiMa.ai and it appears that they may have worked with TSMC's ecosystem partners via the TSMC Open Innovation Platform (OIP) in developing it's MLSoC.
On TSMC's website it says:
TSMC's Open Innovation® model brings together the creative thinking of customers and partners under the common goal of shortening each of the following: design time, time-to-volume, time-to-market and, ultimately, time-to-revenue. The model features:
This made me wonder if BrainChip is involved in TSMC's Open Innovation Platform. I didn't find anything formal to indicate this to be the case but I did stumble on a Pitt Street Research report which states "BRN will have TSMC manufacture Akida chips, which it can then sell directly to customers".
I thought this was very interesting as it's obviously another avenue for AKIDA to be incorporated in products without us being any the wiser.
Something to ponder over.
I was doing some more research on SiMa.ai and it appears that they may have worked with TSMC's ecosystem partners via the TSMC Open Innovation Platform (OIP) in developing it's MLSoC.
On TSMC's website it says:
TSMC's Open Innovation® model brings together the creative thinking of customers and partners under the common goal of shortening each of the following: design time, time-to-volume, time-to-market and, ultimately, time-to-revenue. The model features:
- the foundry segment's earliest and most comprehensive electronic design automation certification program, delivering timely design tool enhancement required by new process technologies;
- the foundry segment's largest, most comprehensive and robust silicon-proven IP (intellectual properties) and library portfolio; and
- comprehensive design ecosystem alliance programs covering market-leading EDA, library, IPs, Cloud, and design service partners.
This made me wonder if BrainChip is involved in TSMC's Open Innovation Platform. I didn't find anything formal to indicate this to be the case but I did stumble on a Pitt Street Research report which states "BRN will have TSMC manufacture Akida chips, which it can then sell directly to customers".
I thought this was very interesting as it's obviously another avenue for AKIDA to be incorporated in products without us being any the wiser.
Something to ponder over.
Makeme 2020
Regular
Renesas buys another company entering the Radar market.
Renesas has acquired Steradian, a fabless semiconductor supplier specializing in radar ICs, to bolster its presence in ADAS and industrial applications.
Acquisition enables Renesas to provide complete portfolio of automotive and industrial sensing solutions.
Sep 19, 2022
Seeking to carve out a presence in the automotive radar market, semiconductor supplier Renesas Electronics Corporatiorecently signed a definitive agreement to acquire Steradian Semiconductors Private Ltd., a fabless semiconductor company based in Bengaluru, India that provides 4D imaging radar solutions. The all-cash acquisition is expected to close by the end of 2022.
According to Renesas, the acquisition of Steradian's radar technology gives the semiconductor supplier a stronger presence in the radar market and boosts its automotive and industrial sensing solution offerings.
Related: Latest Cadence DSP Cores Target Radar, LiDAR Apps
With advancements of ADAS (Advanced Driver Assistance Systems) in the automotive market, automotive sensor fusion demand is growing to allow precise and accurate object detection of vehicles’ surroundings by combining data from multiple sensors, such as cameras, radar, and LiDAR (Light Detection and Ranging). Radar accurately detects objects over long distances, day or night, even during harsh weather or other adverse environmental conditions. Because radar is considered an essential sensing technology for ADAS, the number of radar sensors installed in vehicles is expected to substantially increaes over the next five years, prompting Renesas to expand its automotive product portfolio with Steradian’s radar technology.
“Radar is an indispensable technology for ADAS, which uses a complex combination of various sensors,” said Hidetoshi Shibata, President and CEO of Renesas, in a statement. “The addition of Steradian's superb radar technology and engineering talent will allow us to extend our leadership in the automotive segments. We will also leverage their technology for industrial applications to drive our mid- to long-term business growth in both segments.”
Related: Bosch is Gunning for a Bigger Slice of the Automotive Semiconductor Business
Founded in 2016 as a start-up company, Steradian has extensive expertise in radar technology. Operating in the 76-81 GHz band, Steradian’s powerful 4D radar transceivers offer a high level of integration in a small form factor and high power efficiency. Renesas will leverage Steradian's design assets and expertise to develop automotive radar products, with plans to start sample shipments by the end of 2022. The company aims to develop complete automotive radar solutions that combine ADAS SoCs (System-on-Chips) for processing radar signals, power management ICs (PMICs), and timing products together with software for object recognition. These solutions will simplify the design of automotive radar systems and help speed product development.
Renesas and Steradian have been collaborating since 2018, mainly in industrial applications. Steradian's radar technology is expected to be adopted in home security systems such as surveillance, traffic monitoring for people, cars and motorcycles, HMI (Human-Machine Interface) systems such as gesture recognition and docking systems in airport terminals. Steradian provides targeted solutions for these applications by offering transceiver ICs, turnkey modules that include antennas, and software stacks for object recognition.
Spencer Chin is a Senior Editor for Design News covering the electronics beat. He has many years of experience covering developments in components, semiconductors, subsystems, power, and other facets of electronics from both a business/supply-chain and technology perspective. He can be reached at Spencer.Chin@informa.com.
Renesas Acquires Fabless Semi Company to Enter Radar Market
Image courtesy of Steradian
Renesas has acquired Steradian, a fabless semiconductor supplier specializing in radar ICs, to bolster its presence in ADAS and industrial applications.
Acquisition enables Renesas to provide complete portfolio of automotive and industrial sensing solutions.
Sep 19, 2022
Seeking to carve out a presence in the automotive radar market, semiconductor supplier Renesas Electronics Corporatiorecently signed a definitive agreement to acquire Steradian Semiconductors Private Ltd., a fabless semiconductor company based in Bengaluru, India that provides 4D imaging radar solutions. The all-cash acquisition is expected to close by the end of 2022.
According to Renesas, the acquisition of Steradian's radar technology gives the semiconductor supplier a stronger presence in the radar market and boosts its automotive and industrial sensing solution offerings.
Related: Latest Cadence DSP Cores Target Radar, LiDAR Apps
With advancements of ADAS (Advanced Driver Assistance Systems) in the automotive market, automotive sensor fusion demand is growing to allow precise and accurate object detection of vehicles’ surroundings by combining data from multiple sensors, such as cameras, radar, and LiDAR (Light Detection and Ranging). Radar accurately detects objects over long distances, day or night, even during harsh weather or other adverse environmental conditions. Because radar is considered an essential sensing technology for ADAS, the number of radar sensors installed in vehicles is expected to substantially increaes over the next five years, prompting Renesas to expand its automotive product portfolio with Steradian’s radar technology.
“Radar is an indispensable technology for ADAS, which uses a complex combination of various sensors,” said Hidetoshi Shibata, President and CEO of Renesas, in a statement. “The addition of Steradian's superb radar technology and engineering talent will allow us to extend our leadership in the automotive segments. We will also leverage their technology for industrial applications to drive our mid- to long-term business growth in both segments.”
Related: Bosch is Gunning for a Bigger Slice of the Automotive Semiconductor Business
Founded in 2016 as a start-up company, Steradian has extensive expertise in radar technology. Operating in the 76-81 GHz band, Steradian’s powerful 4D radar transceivers offer a high level of integration in a small form factor and high power efficiency. Renesas will leverage Steradian's design assets and expertise to develop automotive radar products, with plans to start sample shipments by the end of 2022. The company aims to develop complete automotive radar solutions that combine ADAS SoCs (System-on-Chips) for processing radar signals, power management ICs (PMICs), and timing products together with software for object recognition. These solutions will simplify the design of automotive radar systems and help speed product development.
Renesas and Steradian have been collaborating since 2018, mainly in industrial applications. Steradian's radar technology is expected to be adopted in home security systems such as surveillance, traffic monitoring for people, cars and motorcycles, HMI (Human-Machine Interface) systems such as gesture recognition and docking systems in airport terminals. Steradian provides targeted solutions for these applications by offering transceiver ICs, turnkey modules that include antennas, and software stacks for object recognition.
Spencer Chin is a Senior Editor for Design News covering the electronics beat. He has many years of experience covering developments in components, semiconductors, subsystems, power, and other facets of electronics from both a business/supply-chain and technology perspective. He can be reached at Spencer.Chin@informa.com.
Makeme 2020
Regular
AI at the edge: 5 trends to watch
by Megan Crouse in Edge
on September 19, 2022, 11:46 AM PDT
Edge AI offers opportunities for multiple applications. See what organizations are doing to incorporate it today and going forward.
AI at the edge continues to develop. AI applications on the edge are myriad: Autonomous vehicles, art, health care, personalized advertising and customer service could all make use of it. Ideally, edge architecture delivers low latency on account of being closer to the requests.
SEE: Don’t curb your enthusiasm: Trends and challenges in edge computing (TechRepublic)
Astute Analytica predicts the edge AI market will grow from $1.4 million in 2021 to $8 million by 2027, a CAGR of 29.8%. They expect this growth will come in large part from AI for the Internet of Things, wearable consumer devices and a need for faster computing in 5G networks, among other factors. These bring both opportunity and reservation because edge AI’s real-time data is vulnerable to cyberattacks.
Take a look at five trends likely to shape the field of edge AI in the next year.
Top 5 edge AI trends
Separating AI from the cloud
One of today’s sea changes is the ability to run AI processing without a cloud connection. Arm recently released two new chip designs which can bring processing power to the edge for IoT devices, skipping either a remote server or the cloud. Their current Cortex-M processor can handle object recognition, with other abilities such as gesture or speech recognition coming into play with the addition of ARM’s Ethos-U55. Google’s Coral, a toolkit to build products with local AI, also promises hefty AI processing “offline.”Machine learning ops
NVIDIA predicts that best practices in machine learning operations will prove a valuable business process for edge AI. It needs a new lifecycle for IT production — or, at least, that’s the speculation as MLOps develops. MLOps could help organize and push the flow of data to the edge. A continuous cycle of updates may prove effective as more organizations find out what works best for them when it comes to edge AI.More must-read AI coverage
- 8 most innovative AI and machine learning companies
- Artificial intelligence: Cheat sheet
- 3 questions to separate AI from marketing hype
- Hiring Kit: Artificial Intelligence Architect
That means “it’s possible for neural nets to design neural nets,” said Google CEO Sundar Pichai.
Auto ML requires a lot of memory and computational power, so its deployment at the edge goes hand-in-hand with other ongoing processing considerations.
Specialized chips
In order to do more processing on the edge, companies need custom chips to deliver sufficient power. Last year, startup DeepVision made headlines with a $35 million series B financing round for its video analytics and natural language processing chip for the edge.“We expect 1.9 billion edge devices to ship with deep learning accelerators in 2025,” Linley Group principal analyst Linley Gwennap explained.
DeepVision’s AI accelerator chip pairs with a software suite that essentially transforms AI models into computation graphs. IBM released their first accelerator hardware in 2021, intended to battle against fraud.
New use cases and capabilities for computer vision
Computer vision remains one of the prominent uses for edge AI. NVIDIA’s partner network for its application framework and set of developer tools includes over 1,000 members today.A major development in this area is multimodal AI, which pulls from multiple data sources to go beyond natural language understanding into analyzing poses and performing inspection and visualization. This could come in handy for AI which seamlessly interacts with people, such as shopping assistants.
Higher-order vision algorithms can now classify objects by using more granular features. Instead of recognizing a car, it can go deeper to pinpoint the make and model.
Training a model to recognize which granular features are unique to each object can be difficult. However, approaches such as feature representations with fine-grained information, segmentation to extract specific features, algorithms that normalize the pose of an object and multiple-layer convolutional neural networks are all current ways to enable this.
Enterprise use cases in their infancy include quality control, live supply chain tracking, identifying an interior location using a snapshot and detecting deep fakes.
Increased growth of AI on 5G
5G and beyond are almost here. Satellite networks and 6G wait on the horizon for telecommunications providers. For the rest of us, there’s still some time to transition between 4G core networks that work with some 5G services before jumping fully into the next generation.Where does this touch edge AI? AI on 5G could lend greater performance and security to AI applications. It could provide some of that low latency edge AI requires, as well as opening up new applications such as factory automation, tolling and vehicle telemetry, and smart supply chain projects. Mavenir introduced edge AI with 5G for video analytics in November 2021.
There are more emerging trends in edge AI than we can fit on one list. In particular, its proliferation might require some change on the human side as well. NVIDIA predicts edge AI management will become a job for IT, likely using Kubernetes. Using IT resources instead of having the line of business manage edge solutions can optimize costs, Gartner reported.
Dozzaman1977
Regular
Bravo
If ARM was an arm, BRN would be its biceps💪!
Quite interesting to see this article published in Teslarati.
www.teslarati.com
Mercedes-Benz says Drive Pilot is world's first internationally certified Level 3 automated car tech
Mercedes-Benz announced that its Drive Pilot automated driver assistance system (ADAS) is the world's first internationally certified Level 3
www.teslarati.com
clip
Regular
Yes, the same year when BrainChip has shipped their Evaluation Boards.This press release is 2 and a half years old!!!!!
So 1. Gen of Body-Cams without AKIDA, maybe 2. Gen with AKIDA inside.
IMHO Bodycams could even more benefit from AKIDA than stationary cams in terms of power consumption, (bandwidth / internet connection, when the bodycamera is streaming) and it can also be trained to trigger an alarm, if it detects a weapon, for example.
Good point!
Fact Finder
Top 20
Great work. I have been out but here is the evidence of your achievement:View attachment 17025
I noticed that the brainchip was not listed as Partners on SiFive's website and notified them yesterday.
They've already fixed it and confirmed back to me just then.
Software Tools, OS, and IP Partners
- Support for SiFive Core IP
- Compilers, Debuggers, IDEs
- OS, RTOS
- Simulation Models
- Safety Certification and Services
TechGirl
Founding Member
View attachment 17025
I noticed that the brainchip was not listed as Partners on SiFive's website and notified them yesterday.
They've already fixed it and confirmed back to me just then.
Thats great, thanks for the effort.
Partners page now looks awesome with our name on it
Ecosystem Partners - SiFive
View SiFive ecosystem partners that support the RISC-V revolution.
www.sifive.com
Fact Finder
Top 20
Great work. I have been out but here is the evidence of your achievement:
Software Tools, OS, and IP Partners
- Support for SiFive Core IP
- Compilers, Debuggers, IDEs
- OS, RTOS
- Simulation Models
- Safety Certification and Services
The following is the best evidence I can find of where SiFive and Brainchip come together:
1. FROM THE PARTNERSHIP PRESS RELEASE:
“Employing Akida, BrainChip’s specialized, differentiated AI engine, with high-performance RISC-V processors such as the SiFive Intelligence Series is a natural choice for companies looking to seamlessly integrate an optimized processor to dedicated ML accelerators that are a must for the demanding requirements of edge AI computing,” said Chris Jones, vice president, products at SiFive. “BrainChip is a valuable addition to our ecosystem portfolio”.
2. FROM THE SiFive WEBSITE:
iFive Intelligence
SiFive Intelligence X280
The SiFive Intelligence™ X280 is a multi-core capable RISC-V processor with vector extensions and SiFive Intelligence Extensions and is optimized for AI/ML compute at the edge.
In addition to ML inferencing, it is ideal for applications requiring high-throughput, single-thread performance while under power constraints (e.g., AR, VR, sensor hubs, IVI systems, IP cameras, digital cameras, gaming devices).
Download X280 and X280-MC Data Sheet >
My opinion only DYOR
FF
AKIDA BALLISTA
I would like to also ask Valeo for not mentioning us on their website but I don't want to piss them off so I will let that slide and be happy that they are working with us lolView attachment 17025
I noticed that the brainchip was not listed as Partners on SiFive's website and notified them yesterday.
They've already fixed it and confirmed back to me just then.
IMO
Last edited:
Fullmoonfever
Top 20
Just saw this in a search and explains the Edge Impulse process pretty well.
Search said from 4 days ago but probs on the Edge site...didn't look haha
We get a few mentions throughout
www.iotworlds.com
Edge Impulse is a software as a service (SaaS) platform that uses a compiler to turn TensorFlow Lite models into C++ programs. The platform works with existing tools and is designed to compete with startups. This article explores Edge Impulse’s unique capabilities and competitive positioning.
Edge Impulse also has a relationship with Arm. Shelby’s previous startup, Sensinode, was acquired by Arm in 2013. Sensinode provided low-power mesh networking and Internet gateway systems. The two companies were able to work together on end-to-end solutions that covered tel infrastructure and embedded devices. The acquisition gave Arm access to a range of compute power.
Edge Impulse is available as a free SaaS platform for developers. It includes all of the steps needed to build a machine-learning model, from data collection to signal processing and deployment to the sensor. It is free to use for individual developers, but there is a paid version for enterprise customers. The SaaS platform is a powerful tool for embedded engineers looking to make machine-learning solutions for their applications.
Edge Impulse is the leading development platform for machine learning on edge devices. It simplifies the process of developing and testing ML models on edge devices by streamlining data collection and integration. It then validates the models against real-world data. And finally, it deploys optimized models to edge targets, unlocking massive value across every industry. With the platform, millions of developers and businesses can now build and deploy machine-learning applications on billions of devices.
Edge Impulse has received several awards for its EON Tuner, an algorithm that automatically selects the most suitable machine learning model for the edge. It also supports the BrainChip MetaTF platform, which helps developers quickly develop enterprise-grade ML algorithms. To learn more about Edge Impulse, check out the free hour-long webinar.
Edge Impulse works with TensorFlow Lite, an open-source deep learning framework. It is designed for on-device machine learning inference, and it is lightweight and low-latency. Its architecture allows for efficient model conversion, and it uses a compiler that translates TensorFlow Lite models into human-readable C++ programs. This allows it to run on a wide range of hardware, including devices with low-power MCUs.
The TinyML algorithm is designed to detect three different types of geometry. Edge Impulse implements it with its C++ SDK and TensorFlow support. It can also be deployed using a custom PCB. It can also run in standby mode. In addition, TinyML models can be used to filter sensor data.
The Edge Impulse SDK provides a number of useful examples. For instance, the vacuum-recognition demo contains examples and data. This data can be downloaded separately from the GitHub repository. The data used for this demonstration is the COCO dataset.
The model is optimized for low latency, which is important when it is deployed at the edge. By reducing the computational costs, it is possible to produce a model that uses less memory. Optimizing the model reduces its size while preserving its accuracy. Moreover, it allows for a model to store its data as graphs or 32-bit floating-point values.
Edge Impulse also provides support for data forwarding. By leveraging UART connectivity, users can use the CLI to classify sensor data. The Edge Impulse studio also enables customization of data processing, learning, and optimization.
Edge Impulse can also be used to build ML models. This platform has a range of built-in tools and libraries that will make it easy to train ML models. Its CLI supports capturing data from serial ports, CSV files, and JSON files.
The software also allows you to build custom models without coding. There are 3 basic building blocks you must use to build a model. The first one, input block, is used to specify the type of data you want to input to the model. This can be images or time series.
Edge Impulse’s AI platform is available as a free and enterprise version. The free version has some limitations, such as a single developer’s sweat and a cloud storage limit of four GB. The enterprise version, which costs $149 per project, removes these restrictions and allows for up to five users per project.
Edge Impulse enables the development of enterprise-grade ML algorithms that train on real sensor data. These models can be quantised and optimised. Then, they can be deployed on BrainChip Akida devices. Enterprise developers can also leverage the BrainChip MetaTF model deployment block to deploy neuromorphic models.
Edge Impulse is free and easy to use. It helps speed up data pre-processing and model building. It features a user-friendly UI that guides you through the process and allows you to customize your model. It also provides a TensorFlow-lite model library that supports all popular formats.
Edge Impulse’s AI technology is based on the BrainChip Akida processor, a breakthrough neural networking processor architecture that delivers high performance and ultra-low power, while still allowing for on-chip learning. It also enables you to visualize the results of your inference using any web browser.
The startup uses off-the-shelf machine learning frameworks such as TensorFlow to make its models as easy to use as possible. It also provides tools for domain experts to collect data, classify it, and predict the future. Those features are also available in the free tier of Edge Impulse. The company also offers a subscription option that allows customers to gain access to features like collaboration between multiple engineers, larger datasets, and model versioning.
Edge Impulse’s platform makes it easier to build smarter IoT applications. It supports sensor, audio, and computer vision applications. It can also help with asset tracking and health applications. In addition, it ingests 99 percent of critical sensor data, which improves the performance of its algorithms. This technology also enables developers to quickly and easily create new applications.
Edge Impulse has recently raised $34 million in Series B funding. This investment will allow the startup to expand its operations, marketing, and staff. The company also plans to double its annual recurring revenue and triple its market valuation by 2022. Its current investors include Coatue, Sequoia Capital, and Accel.
As a SaaS platform, the company offers developers a solution to implement TinyML in their enterprise environments. Its SaaS platform includes the entire set of steps that is necessary to build models: data collection, signal processing, and deployment to a sensor. It’s available for free to individual developers, as well as a paid service for enterprise customers.
The vid appears to be from Nov 2021.
Search said from 4 days ago but probs on the Edge site...didn't look haha
We get a few mentions throughout
Edge Impulse – Making Machine Learning Available for Embedded Devices
Edge Impulse is a software as a service (SaaS) platform that uses a compiler to turn TensorFlow Lite models into C++ programs. The platform works with existing tools and is designed to compete with startups. This article explores Edge Impulse's unique capabilities and competitive positioning...
www.iotworlds.com
Edge Impulse – Making Machine Learning Available for Embedded Devices
Edge Impulse is a software as a service (SaaS) platform that uses a compiler to turn TensorFlow Lite models into C++ programs. The platform works with existing tools and is designed to compete with startups. This article explores Edge Impulse’s unique capabilities and competitive positioning.
Edge Impulse is a software as a service platform
Edge Impulse is a software as ta service platform that makes machine learning available for embedded devices. Launched in mid-2019, the platform already boasts a growing list of enterprise customers including Oura, Polycom, and NASA. Its goal is to help customers deploy machine learning on their embedded devices and achieve high-impact results.Edge Impulse also has a relationship with Arm. Shelby’s previous startup, Sensinode, was acquired by Arm in 2013. Sensinode provided low-power mesh networking and Internet gateway systems. The two companies were able to work together on end-to-end solutions that covered tel infrastructure and embedded devices. The acquisition gave Arm access to a range of compute power.
Edge Impulse is available as a free SaaS platform for developers. It includes all of the steps needed to build a machine-learning model, from data collection to signal processing and deployment to the sensor. It is free to use for individual developers, but there is a paid version for enterprise customers. The SaaS platform is a powerful tool for embedded engineers looking to make machine-learning solutions for their applications.
Edge Impulse is the leading development platform for machine learning on edge devices. It simplifies the process of developing and testing ML models on edge devices by streamlining data collection and integration. It then validates the models against real-world data. And finally, it deploys optimized models to edge targets, unlocking massive value across every industry. With the platform, millions of developers and businesses can now build and deploy machine-learning applications on billions of devices.
Edge Impulse has received several awards for its EON Tuner, an algorithm that automatically selects the most suitable machine learning model for the edge. It also supports the BrainChip MetaTF platform, which helps developers quickly develop enterprise-grade ML algorithms. To learn more about Edge Impulse, check out the free hour-long webinar.
It uses a compiler that converts TensorFlow Lite models into human readable C++ programs
Edge Impulse is a platform that uses a Tensorflow Lite compiler to build deep learning models on embedded devices. The resulting model can be deployed to any device, whether it be a smartphone, tablet, or PC. It is a cross-platform and open-source platform that makes it easy to train models and deploy them at the Edge. It works on Linux-based embedded devices and mobile devices.Edge Impulse works with TensorFlow Lite, an open-source deep learning framework. It is designed for on-device machine learning inference, and it is lightweight and low-latency. Its architecture allows for efficient model conversion, and it uses a compiler that translates TensorFlow Lite models into human-readable C++ programs. This allows it to run on a wide range of hardware, including devices with low-power MCUs.
The TinyML algorithm is designed to detect three different types of geometry. Edge Impulse implements it with its C++ SDK and TensorFlow support. It can also be deployed using a custom PCB. It can also run in standby mode. In addition, TinyML models can be used to filter sensor data.
The Edge Impulse SDK provides a number of useful examples. For instance, the vacuum-recognition demo contains examples and data. This data can be downloaded separately from the GitHub repository. The data used for this demonstration is the COCO dataset.
The model is optimized for low latency, which is important when it is deployed at the edge. By reducing the computational costs, it is possible to produce a model that uses less memory. Optimizing the model reduces its size while preserving its accuracy. Moreover, it allows for a model to store its data as graphs or 32-bit floating-point values.
Edge Impulse also provides support for data forwarding. By leveraging UART connectivity, users can use the CLI to classify sensor data. The Edge Impulse studio also enables customization of data processing, learning, and optimization.
Edge Impulse can also be used to build ML models. This platform has a range of built-in tools and libraries that will make it easy to train ML models. Its CLI supports capturing data from serial ports, CSV files, and JSON files.
It integrates seamlessly with existing tools
With Edge Impulse, you can build AI applications using familiar and well documented methods. The tools in this software suite can be combined to achieve a variety of goals, from detecting anomalies to analyzing signal patterns. They provide several different analysis methods, including signal flattening and analysis of repetitive motion.The software also allows you to build custom models without coding. There are 3 basic building blocks you must use to build a model. The first one, input block, is used to specify the type of data you want to input to the model. This can be images or time series.
Edge Impulse’s AI platform is available as a free and enterprise version. The free version has some limitations, such as a single developer’s sweat and a cloud storage limit of four GB. The enterprise version, which costs $149 per project, removes these restrictions and allows for up to five users per project.
Edge Impulse enables the development of enterprise-grade ML algorithms that train on real sensor data. These models can be quantised and optimised. Then, they can be deployed on BrainChip Akida devices. Enterprise developers can also leverage the BrainChip MetaTF model deployment block to deploy neuromorphic models.
Edge Impulse is free and easy to use. It helps speed up data pre-processing and model building. It features a user-friendly UI that guides you through the process and allows you to customize your model. It also provides a TensorFlow-lite model library that supports all popular formats.
Edge Impulse’s AI technology is based on the BrainChip Akida processor, a breakthrough neural networking processor architecture that delivers high performance and ultra-low power, while still allowing for on-chip learning. It also enables you to visualize the results of your inference using any web browser.
It competes with startups
Edge Impulse is a startup that uses machine learning to build smarter embedded devices. The company launched in mid-2019 and has almost 30,000 developers using its platform. Its customers include NASA, Polycom, and Advantech. In a recent funding round, Edge Impulse raised $34 million from investors including Coatue, Momenta Ventures, and Acrew Capital.The startup uses off-the-shelf machine learning frameworks such as TensorFlow to make its models as easy to use as possible. It also provides tools for domain experts to collect data, classify it, and predict the future. Those features are also available in the free tier of Edge Impulse. The company also offers a subscription option that allows customers to gain access to features like collaboration between multiple engineers, larger datasets, and model versioning.
Edge Impulse’s platform makes it easier to build smarter IoT applications. It supports sensor, audio, and computer vision applications. It can also help with asset tracking and health applications. In addition, it ingests 99 percent of critical sensor data, which improves the performance of its algorithms. This technology also enables developers to quickly and easily create new applications.
Edge Impulse has recently raised $34 million in Series B funding. This investment will allow the startup to expand its operations, marketing, and staff. The company also plans to double its annual recurring revenue and triple its market valuation by 2022. Its current investors include Coatue, Sequoia Capital, and Accel.
As a SaaS platform, the company offers developers a solution to implement TinyML in their enterprise environments. Its SaaS platform includes the entire set of steps that is necessary to build models: data collection, signal processing, and deployment to a sensor. It’s available for free to individual developers, as well as a paid service for enterprise customers.
The vid appears to be from Nov 2021.
Fullmoonfever
Top 20
Be nice if Antonio could be a bridge between BRN, Arteris & ARM for some overlapping tech huh
Definitely play in the same space.
www.arteris.com
CAMPBELL, Calif. – September 12, 2022 – Arteris, Inc. (Nasdaq: AIP), a leading provider of system IP which accelerates system-on-chip (SoC) creation, today announced that it is expanding its partnership with Arm to help speed up automotive electronics innovation. This collaboration leverages leading-edge Arm® processors with Arteris system IP to enable autonomous driving, advanced driver assistance systems (ADAS), cockpit and infotainment, vision, radar and lidar, body and chassis control, and other automotive subsystems. The partnership delivers solutions that could hasten the path for customers to realize SoCs with high performance and power efficiency for complex and demanding safety-critical tasks with differing workloads while reducing project schedules and costs.
“The automotive industry is at a critical inflection point with demand for autonomy, more capable ADAS, richer driver experiences and electrification driving the need for more capable SoCs and microcontroller units (MCUs),” said Ian Smythe, vice president product marketing at Arm. “Our expanded collaboration with Arteris gives our mutual customers access to a greater choice of market-leading, safe, integrated and optimized automotive solutions, enabling faster time to market.”
Designers creating automotive electronics use Arm’s broad portfolio for core compute leveraging the Arm AE roadmap including Cortex®-A processors, Cortex-R, Cortex-M, and Mali™. Developers also depend on Arteris system IP consisting of FlexNoC® and Ncore® interconnect IP and Magillem® IP deployment software to assemble automotive SoCs. This extended partnership means that Arteris and Arm are now delivering customer success via seamless integration and optimized flows with the highest quality of results, enabling ISO 26262 systems with the highest automotive safety integrity levels and well-aligned roadmaps to solve current and future automotive SoC design challenges.
“Arteris continues to see very strong demand for automotive systems because of growing demands for intelligence and sensing and the resulting need for high-performance compute with advanced system-on-chip connectivity,” said K. Charles Janac, president and CEO of Arteris, Inc. “We are delighted to extend our partnership with Arm to accelerate best-in-class solutions to support semiconductor companies, Tier 1 suppliers, automotive OEMs, and ride-sharing companies in creating the new world of transportation.”
To learn more about these solutions, visit https://www.arm.com/partners/automotive-ecosystem-catalog/arteris-ip
Definitely play in the same space.
Expanded Partnership Between Arteris and Arm to Accelerate Automotive Electronics - Arteris
Arm and Arteris expand partnership to accelerate automotive electronics.
Expanded Partnership Between Arteris and Arm to Accelerate Automotive Electronics
by Arteris Marketing, on September 12, 2022The collaboration will provide a greater choice of integrated and optimized solutions with leading Arm processors and Arteris system IP.
CAMPBELL, Calif. – September 12, 2022 – Arteris, Inc. (Nasdaq: AIP), a leading provider of system IP which accelerates system-on-chip (SoC) creation, today announced that it is expanding its partnership with Arm to help speed up automotive electronics innovation. This collaboration leverages leading-edge Arm® processors with Arteris system IP to enable autonomous driving, advanced driver assistance systems (ADAS), cockpit and infotainment, vision, radar and lidar, body and chassis control, and other automotive subsystems. The partnership delivers solutions that could hasten the path for customers to realize SoCs with high performance and power efficiency for complex and demanding safety-critical tasks with differing workloads while reducing project schedules and costs.
“The automotive industry is at a critical inflection point with demand for autonomy, more capable ADAS, richer driver experiences and electrification driving the need for more capable SoCs and microcontroller units (MCUs),” said Ian Smythe, vice president product marketing at Arm. “Our expanded collaboration with Arteris gives our mutual customers access to a greater choice of market-leading, safe, integrated and optimized automotive solutions, enabling faster time to market.”
Designers creating automotive electronics use Arm’s broad portfolio for core compute leveraging the Arm AE roadmap including Cortex®-A processors, Cortex-R, Cortex-M, and Mali™. Developers also depend on Arteris system IP consisting of FlexNoC® and Ncore® interconnect IP and Magillem® IP deployment software to assemble automotive SoCs. This extended partnership means that Arteris and Arm are now delivering customer success via seamless integration and optimized flows with the highest quality of results, enabling ISO 26262 systems with the highest automotive safety integrity levels and well-aligned roadmaps to solve current and future automotive SoC design challenges.
“Arteris continues to see very strong demand for automotive systems because of growing demands for intelligence and sensing and the resulting need for high-performance compute with advanced system-on-chip connectivity,” said K. Charles Janac, president and CEO of Arteris, Inc. “We are delighted to extend our partnership with Arm to accelerate best-in-class solutions to support semiconductor companies, Tier 1 suppliers, automotive OEMs, and ride-sharing companies in creating the new world of transportation.”
To learn more about these solutions, visit https://www.arm.com/partners/automotive-ecosystem-catalog/arteris-ip
About Arteris IP
Arteris is a leading provider of system IP, consisting of network-on-chip (NoC) interconnect IP and IP deployment technology to accelerate system-on-chip (SoC) semiconductor development and integration for a wide range of electronic products. Vertical applications include automotive, mobile, consumer electronics, enterprise datacenters, 5G communications, industrial and IoT, leveraging technologies such as AI/ML and functional safety for customers such as BMW, Bosch, Baidu, Mobileye, Samsung, Toshiba and NXP. Arteris IP products include the FlexNoC® interconnect IP, Ncore® cache coherent IP, CodaCache® standalone last level cache, ISO 26262 safety, Artificial Intelligence, automated timing closure and Magillem SoC assembly automation. Customer results obtained by deploying Arteris IP include higher performance, lower power and area, more efficient design reuse and faster SoC development, leading to lower development and production costs.Fact Finder
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Just saw this in a search and explains the Edge Impulse process pretty well.
Search said from 4 days ago but probs on the Edge site...didn't look haha
We get a few mentions throughout
Edge Impulse – Making Machine Learning Available for Embedded Devices
Edge Impulse is a software as a service (SaaS) platform that uses a compiler to turn TensorFlow Lite models into C++ programs. The platform works with existing tools and is designed to compete with startups. This article explores Edge Impulse's unique capabilities and competitive positioning...
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Edge Impulse – Making Machine Learning Available for Embedded Devices
Edge Impulse is a software as a service (SaaS) platform that uses a compiler to turn TensorFlow Lite models into C++ programs. The platform works with existing tools and is designed to compete with startups. This article explores Edge Impulse’s unique capabilities and competitive positioning.
Edge Impulse is a software as a service platform
Edge Impulse is a software as ta service platform that makes machine learning available for embedded devices. Launched in mid-2019, the platform already boasts a growing list of enterprise customers including Oura, Polycom, and NASA. Its goal is to help customers deploy machine learning on their embedded devices and achieve high-impact results.
Edge Impulse also has a relationship with Arm. Shelby’s previous startup, Sensinode, was acquired by Arm in 2013. Sensinode provided low-power mesh networking and Internet gateway systems. The two companies were able to work together on end-to-end solutions that covered tel infrastructure and embedded devices. The acquisition gave Arm access to a range of compute power.
Edge Impulse is available as a free SaaS platform for developers. It includes all of the steps needed to build a machine-learning model, from data collection to signal processing and deployment to the sensor. It is free to use for individual developers, but there is a paid version for enterprise customers. The SaaS platform is a powerful tool for embedded engineers looking to make machine-learning solutions for their applications.
Edge Impulse is the leading development platform for machine learning on edge devices. It simplifies the process of developing and testing ML models on edge devices by streamlining data collection and integration. It then validates the models against real-world data. And finally, it deploys optimized models to edge targets, unlocking massive value across every industry. With the platform, millions of developers and businesses can now build and deploy machine-learning applications on billions of devices.
Edge Impulse has received several awards for its EON Tuner, an algorithm that automatically selects the most suitable machine learning model for the edge. It also supports the BrainChip MetaTF platform, which helps developers quickly develop enterprise-grade ML algorithms. To learn more about Edge Impulse, check out the free hour-long webinar.
It uses a compiler that converts TensorFlow Lite models into human readable C++ programs
Edge Impulse is a platform that uses a Tensorflow Lite compiler to build deep learning models on embedded devices. The resulting model can be deployed to any device, whether it be a smartphone, tablet, or PC. It is a cross-platform and open-source platform that makes it easy to train models and deploy them at the Edge. It works on Linux-based embedded devices and mobile devices.
Edge Impulse works with TensorFlow Lite, an open-source deep learning framework. It is designed for on-device machine learning inference, and it is lightweight and low-latency. Its architecture allows for efficient model conversion, and it uses a compiler that translates TensorFlow Lite models into human-readable C++ programs. This allows it to run on a wide range of hardware, including devices with low-power MCUs.
The TinyML algorithm is designed to detect three different types of geometry. Edge Impulse implements it with its C++ SDK and TensorFlow support. It can also be deployed using a custom PCB. It can also run in standby mode. In addition, TinyML models can be used to filter sensor data.
The Edge Impulse SDK provides a number of useful examples. For instance, the vacuum-recognition demo contains examples and data. This data can be downloaded separately from the GitHub repository. The data used for this demonstration is the COCO dataset.
The model is optimized for low latency, which is important when it is deployed at the edge. By reducing the computational costs, it is possible to produce a model that uses less memory. Optimizing the model reduces its size while preserving its accuracy. Moreover, it allows for a model to store its data as graphs or 32-bit floating-point values.
Edge Impulse also provides support for data forwarding. By leveraging UART connectivity, users can use the CLI to classify sensor data. The Edge Impulse studio also enables customization of data processing, learning, and optimization.
Edge Impulse can also be used to build ML models. This platform has a range of built-in tools and libraries that will make it easy to train ML models. Its CLI supports capturing data from serial ports, CSV files, and JSON files.
It integrates seamlessly with existing tools
With Edge Impulse, you can build AI applications using familiar and well documented methods. The tools in this software suite can be combined to achieve a variety of goals, from detecting anomalies to analyzing signal patterns. They provide several different analysis methods, including signal flattening and analysis of repetitive motion.
The software also allows you to build custom models without coding. There are 3 basic building blocks you must use to build a model. The first one, input block, is used to specify the type of data you want to input to the model. This can be images or time series.
Edge Impulse’s AI platform is available as a free and enterprise version. The free version has some limitations, such as a single developer’s sweat and a cloud storage limit of four GB. The enterprise version, which costs $149 per project, removes these restrictions and allows for up to five users per project.
Edge Impulse enables the development of enterprise-grade ML algorithms that train on real sensor data. These models can be quantised and optimised. Then, they can be deployed on BrainChip Akida devices. Enterprise developers can also leverage the BrainChip MetaTF model deployment block to deploy neuromorphic models.
Edge Impulse is free and easy to use. It helps speed up data pre-processing and model building. It features a user-friendly UI that guides you through the process and allows you to customize your model. It also provides a TensorFlow-lite model library that supports all popular formats.
Edge Impulse’s AI technology is based on the BrainChip Akida processor, a breakthrough neural networking processor architecture that delivers high performance and ultra-low power, while still allowing for on-chip learning. It also enables you to visualize the results of your inference using any web browser.
It competes with startups
Edge Impulse is a startup that uses machine learning to build smarter embedded devices. The company launched in mid-2019 and has almost 30,000 developers using its platform. Its customers include NASA, Polycom, and Advantech. In a recent funding round, Edge Impulse raised $34 million from investors including Coatue, Momenta Ventures, and Acrew Capital.
The startup uses off-the-shelf machine learning frameworks such as TensorFlow to make its models as easy to use as possible. It also provides tools for domain experts to collect data, classify it, and predict the future. Those features are also available in the free tier of Edge Impulse. The company also offers a subscription option that allows customers to gain access to features like collaboration between multiple engineers, larger datasets, and model versioning.
Edge Impulse’s platform makes it easier to build smarter IoT applications. It supports sensor, audio, and computer vision applications. It can also help with asset tracking and health applications. In addition, it ingests 99 percent of critical sensor data, which improves the performance of its algorithms. This technology also enables developers to quickly and easily create new applications.
Edge Impulse has recently raised $34 million in Series B funding. This investment will allow the startup to expand its operations, marketing, and staff. The company also plans to double its annual recurring revenue and triple its market valuation by 2022. Its current investors include Coatue, Sequoia Capital, and Accel.
As a SaaS platform, the company offers developers a solution to implement TinyML in their enterprise environments. Its SaaS platform includes the entire set of steps that is necessary to build models: data collection, signal processing, and deployment to a sensor. It’s available for free to individual developers, as well as a paid service for enterprise customers.
The vid appears to be from Nov 2021.
Great post just love how this is worded.
“Edge Impulse’s AI technology is based on the BrainChip Akida processor, a breakthrough neural networking processor architecture that delivers high performance and ultra-low power, while still allowing for on-chip learning. It also enables you to visualize the results of your inference using any web browser.”
ABSOLUTELY NO ROOM FOR DOUBT ABOUT WHERE EDGE IMPULSE GETS ITS ARTIFICIAL INTELLIGENCE FROM!!!
My opinion only DYOR
FF
AKIDA BALLISTA
Fullmoonfever
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Older article by our mate that I found interesting.
I know Silicon Labs has been mentioned before and pondering any connection to Akida via Edge Impulse given they are one of Silicon Labs Tech partners and Silicon Labs now have an edge AI/ML accelerator for their devices. Obviously compatible with M class as well.
www.embedded.com
Silicon Labs’ latest families of wireless-enabled SoCs for IoT applications for the first time include a hardware AI/ML accelerator. The upgrade is indicative of the growing popularity of AI/ML techniques for a variety of IoT markets, including smart home, medical and industrial. Dedicated AI/ML hardware on-chip improves power consumption, critical to many IoT applications, even bringing AI/ML within reach for more power-sensitive IoT applications.
“You’ve always been able to run machine learning algorithms on an M-class processor, the trick is can you do it in an energy efficient way?” said Ross Sabolcik, general manager for IoT industrial and commercial products at Silicon Labs. “If you burn so much energy making the calculations, you might as well push it to the cloud, if you have the bandwidth. Our focus was not only to be able to run AI and ML, but to be able to do it in a really efficient way.”
The BG24 and MG24 families, with Bluetooth capability and multi-protocol capability, respectively, will be the first devices in Silicon Labs’ portfolio to feature a new, in-house developed AI/ML accelerator. The accelerator offloads AI/ML workloads from adjacent Arm Cortex-M33 microcontroller cores in applications such as smart home, medical and industrial IoT.
Sabolcik said the company’s hardware accelerator can speed up IoT AI/ML workloads up to four times with a resulting six-fold power savings (compared to using the Cortex-M33). Such power savings are suitable for battery-powered IoT devices. Latency is also improved, compared to sending data back and forth to the cloud for processing.
The new devices natively support TensorFlow, and Silicon Labs has partnered with SensiML and Edge Impulse for a full toolchain to simplify application development and dataset management.
According to Sabolcik, customers are interested in executing AI/ML workloads at lower power at the network edge in applications like wake-word detection and sound detection in security scenarios. AI would also accelerate predictive maintenance analysis for industrial machinery. For non-AI application, the availability of accelerators could mean better reliability, fewer false positives and improved accuracy, Sabolcik said. Vision use cases, such as presence detection or people counting with low-resolution cameras, are also possible on these devices.
Matter is enabled
The BG24 and MG24 also boost flash and RAM capacities, the largest in Silicon Labs’ portfolio. The memory increase stems not from AI/ML requirements, said Sabolcik, but from the desire to offer future-proofing functionality such as multi-protocol support and over-the-air app upgrades. Both series offer up to 1536 kB of flash and 256 kB of RAM.
“Across the board, this is the most capable device that we know how to build for the 2.4GHz space, in terms of sensing, computing, connectivity and security,” he said.
While the BG24 is designed for Bluetooth applications, the MG24 has multi-protocol support, including Matter. Matter, the home automation connectivity standard previously known as Connected Home over IP, is gaining popularity. The feature represents Silicon Labs’ highest radio output power for range and reliability at +20dBm, and the company’s best sensitivity on the receive side.
Robust security features in both families also meet PSA Level 3.
BG24 and MG24 parts are already shipping to more than 40 early-access customers, with general availability expected in April 2022. Modules based on the new SoCs will be available in the second half of this year, the company said.
that I found interesting.I know Silicon Labs has been mentioned before and pondering any connection to Akida via Edge Impulse given they are one of Silicon Labs Tech partners and Silicon Labs now have an edge AI/ML accelerator for their devices. Obviously compatible with M class as well.
Wireless processors for IoT get AI accelerator upgrade - Embedded.com
Silicon Labs’ latest IoT SoCs include an in-house-developed hardware IoT AI/ML accelerator, reducing power consumption 6x for AI workloads. Silicon Labs’
Wireless processors for IoT get AI accelerator upgrade
February 9, 2022 Sally Ward-FoxtonSilicon Labs’ latest families of wireless-enabled SoCs for IoT applications for the first time include a hardware AI/ML accelerator. The upgrade is indicative of the growing popularity of AI/ML techniques for a variety of IoT markets, including smart home, medical and industrial. Dedicated AI/ML hardware on-chip improves power consumption, critical to many IoT applications, even bringing AI/ML within reach for more power-sensitive IoT applications.
“You’ve always been able to run machine learning algorithms on an M-class processor, the trick is can you do it in an energy efficient way?” said Ross Sabolcik, general manager for IoT industrial and commercial products at Silicon Labs. “If you burn so much energy making the calculations, you might as well push it to the cloud, if you have the bandwidth. Our focus was not only to be able to run AI and ML, but to be able to do it in a really efficient way.”
The BG24 and MG24 families, with Bluetooth capability and multi-protocol capability, respectively, will be the first devices in Silicon Labs’ portfolio to feature a new, in-house developed AI/ML accelerator. The accelerator offloads AI/ML workloads from adjacent Arm Cortex-M33 microcontroller cores in applications such as smart home, medical and industrial IoT.
Sabolcik said the company’s hardware accelerator can speed up IoT AI/ML workloads up to four times with a resulting six-fold power savings (compared to using the Cortex-M33). Such power savings are suitable for battery-powered IoT devices. Latency is also improved, compared to sending data back and forth to the cloud for processing.
The new devices natively support TensorFlow, and Silicon Labs has partnered with SensiML and Edge Impulse for a full toolchain to simplify application development and dataset management.
According to Sabolcik, customers are interested in executing AI/ML workloads at lower power at the network edge in applications like wake-word detection and sound detection in security scenarios. AI would also accelerate predictive maintenance analysis for industrial machinery. For non-AI application, the availability of accelerators could mean better reliability, fewer false positives and improved accuracy, Sabolcik said. Vision use cases, such as presence detection or people counting with low-resolution cameras, are also possible on these devices.
Matter is enabled
The BG24 and MG24 also boost flash and RAM capacities, the largest in Silicon Labs’ portfolio. The memory increase stems not from AI/ML requirements, said Sabolcik, but from the desire to offer future-proofing functionality such as multi-protocol support and over-the-air app upgrades. Both series offer up to 1536 kB of flash and 256 kB of RAM.
“Across the board, this is the most capable device that we know how to build for the 2.4GHz space, in terms of sensing, computing, connectivity and security,” he said.
While the BG24 is designed for Bluetooth applications, the MG24 has multi-protocol support, including Matter. Matter, the home automation connectivity standard previously known as Connected Home over IP, is gaining popularity. The feature represents Silicon Labs’ highest radio output power for range and reliability at +20dBm, and the company’s best sensitivity on the receive side.
Robust security features in both families also meet PSA Level 3.
BG24 and MG24 parts are already shipping to more than 40 early-access customers, with general availability expected in April 2022. Modules based on the new SoCs will be available in the second half of this year, the company said.
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