BRN Discussion Ongoing
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Fastback6666
Regular
General Motors Reaches Deal to Ensure Its Chip Supply (Published 2023)
GlobalFoundries will dedicate part of its semiconductor output to G.M. as the automaker offers more electric vehicles and driver-assistance systems.www.nytimes.com
Interesting link here also about GM and a tie up with Global Foundries one of our new partners.
GM securing chip supply through GF.
Within this article there is an embedded link to a similar article on Ford doing the same thing!
Could be coincidence but who knows!
too funny, but seriously there is some serious ducks being lined up here around our BRN tech
Tothemoon24
Top 20
Recent articles
Avnet Unveils the RASynBoard Edge AI Board, with Renesas MCU, Syntiant NDP, and TDK Sensors
Featuring a microcontroller, edge AI accelerator, Wi-Fi and Bluetooth connectivity, plus on-board sensors, this little board packs a punch.
5 minutes ago • Machine Learning & AI / Internet of Things /
Avnet has announced pre-orders for the RASynBoard, a development kit which combines a Renesas microcontroller and a Syntiant Neural Decision Processor (NDP) with on-board TDK sensors to provide a platform for ultra-low-power edge AI projects — with immediate support in Edge Impulse.
“Avnet’s strong relationship with industry leading suppliers such as Renesas, TDK, and our new supplier Syntiant allows us to combine the unique offerings and capabilities of each partner into a complete system-level, production-ready solution," explains Avnet's Jim Beneke of the new board. "Our design customization and manufacturing capabilities also allow us to offer production-ready solutions for customers needing a full-turnkey option."
: Avnet)The RASynBoard Core Board is a compact module with USB Type-C connectivity which includes a Renesas RA6M4 microcontroller with a single Arm Cortex-M33 core running at 200MHz, 256kB of static RAM, and 1MB of flash memory, a Syntiant NDP120 Neural Decision Processor (NDP) with Core 2 deep neural network hardware, Arm Cortex-M0 core, and Cadence HiFi 3 Digital Signal Processor (DSP). Elsewhere on the board is 2MB of SPI flash, a Renesas DA16600 802.11b/g/n Wi-Fi and Bluetooth 5.1 radio module, a lithium-polymer battery management circuit, and a six-axis inertial measurement unit (IMU) and digital microphone from TDK.
With all of the above in a compact 25×30mm (around 0.98×1.18") footprint, the RASynBoard aims to be a tiny titan for on-device machine learning work — loading models from the SPI flash storage for execution on the NDP120. "Our hardware technology brings advanced multimodal neural processing to Avnet's new RASynBoard with very low power consumption," explains Syntiant's Mallik Moturi. "The NDP120's ability to provide highly accurate, always-on sensor processing with relatively no impact to battery life was a key edge AI requirement in the development of the module, which also makes the device ideal for supporting a wide range of field applications in smart buildings, factories and cities."
For those growing beyond the RASynBoard Core Board's built-in capabilities, two 28-pin board-to-board connectors provide expansion — with an optional IO Board making use of these to offer an on-board debugger and USB-serial interface, a MikroE Click shuttle box header, a Pmod Type-6A socket, a 14-pin microcontroller expansion header, microSD storage, a user-definable button and RGB LED.
: Avnet)On the software front, Avnet has announced a partnership with Edge Impulse to provide support for the RASynBoard within the company's popular Studio machine learning platform. "Edge impulse is excited to collaborate with Avnet on the launch of the RASynBoard, an ideal solution for ultra-low-power machine learning applications thanks to its Renesas MCU and sub-mW Syntiant NDP120 processor, and flexible array of sensors," says Edge Impulse's Raul Vergara. "Customers can quickly develop advanced models in Edge Impulse Studio and deploy them to the board for always-on inferencing in almost any location or environment."
The RASynBoard Core Board and IO Board bundle is now up for pre-order on Avnet's site at $99, with delivery expected to take place late in the second quarter of 2023.
internet of things
Deadpool
Did someone say KFC
Golly TechGirl, this partnership is looking to be sensational for us.Teksun Machine Learning section on their website, again right up our ally & they do Natural Language Processing amongst others.
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Bravo
If ARM was an arm, BRN would be its biceps💪!
Yeah, but you gotta admit you wouldn't be too disappointed if it actually hit 12% as predicted.
Bravo
If ARM was an arm, BRN would be its biceps💪!
Bravo
If ARM was an arm, BRN would be its biceps💪!
Meaning I might need to brush up a bit on my technological prowess as it seems to have absconded somewhere without a trace. This is coming from someone who valiantly tried to read, if not understand Peter Van Der Made's book "Higher Intelligence", but in doing so managed to forget nearly every sentence the second after reading it. Maybe Peter could write his next book using me as test case and this time he could call the book "Lower Intelligence".
Last edited:
Townyj
Ermahgerd
Fact Finder
Top 20
Yes the logic of this did not escape me either:
“Now tell me Mr. Five might I call you Si, how can you say you have a preference to tightly integrate AKIDA S and/or P with X280 when AKIDA E is cheaper and more power efficient?
Yes sir you may call me Si. That is a really good question Freddie.
Well the answer is very simple we don’t just make these statements on a whim.
We have been officially partnered with Brainchip for some time but prior to that our engineers did extensive testing of Brainchip’s AKIDA technology IP family to firstly determine if X280 and AKIDA were compatible and had something in combination to offer to our customers.
Our Engineers having answered yes we then asked our marketing people what was it that our customers were seeking and which was lacking from our present Product line, not just the Intelligence Series, and they came back with quite a long list actually.
We then went back to our engineers with this list and they applied themselves to the task and after extensive further testing and interaction with Brainchip advised that AKIDA S and AKIDA P were the perfect fit for X280.
Thanks Si for that it sounds like you really do your technology due diligence before jumping into bed with your technology partners.”
or something like this but maybe Si and his mates are from the shoot from the hip school of management and make technology adoption decisions based on a magazine article and a coin toss.
My opinion only DYOR
FF
AKIDA BALLISTA
Steve10
Regular
Jobs ads for Sales Executives in USA & Europe for Edge Impulse targeting health monitoring market.
Proven experience with identifying and onboarding enterprise customers in the health monitoring arena.
Proven experience with identifying and onboarding enterprise customers in the health monitoring arena.
Edge Impulse - Senior Enterprise Sales Executive - SaaS (EU)
Do you want to accelerate your SaaS sales career? With Edge Impulse, modern health wearables observe and analyze the human body to deliver new experiences from untapped product data. Edge Impulse accelerates the development of predictive maintenance algorithms using sensor, audio and vision data...
jobs.lever.co
Edge Impulse - Senior Enterprise Sales Executive - SaaS (US)
Do you want to accelerate your SaaS sales career? With Edge Impulse, modern health wearables observe and analyze the human body to deliver new experiences from untapped product data. Edge Impulse accelerates the development of predictive maintenance algorithms using sensor, audio and vision data...
jobs.lever.co
D
Deleted member 118
Guest
White Horse
Regular
https://www.linkedin.com/posts/leddartech_perception-adas-autonomousdriving-ugcPost-7038509033632198656-JMqm?utm_source=share&utm_medium=member_desktop
Rob Telson likes this
LeddarTech- Automotive Software: Low-Level Sensor Fusion & Perception
13,584 followers
1d • Edited •
1 day ago
Are you looking to enhance your knowledge of ADAS and Perception systems? If so, then understanding the concepts of Ego-Lane and CIPV is crucial.
Identifying these vehicles especially when distant is critical for implementation of safety ADAS features such as automatic emergency braking and forward collision warning.
To gain a better insight into the concepts of Ego-Lane and CIPV, check out our informative video that explains how these concepts look from a perception system's point-of-view. It is an excellent opportunity to learn more about these crucial aspects of ADAS and Autonomous Driving.
If you're interested in exploring more about the world of Perception and ADAS, check out this interactive tool - https://hubs.li/Q01F72js0
#perception #ADAS #autonomousdriving
Remaining time 1:18
Rob Telson likes this
LeddarTech- Automotive Software: Low-Level Sensor Fusion & Perception
13,584 followers
1d • Edited •
1 day ago
Are you looking to enhance your knowledge of ADAS and Perception systems? If so, then understanding the concepts of Ego-Lane and CIPV is crucial.Identifying these vehicles especially when distant is critical for implementation of safety ADAS features such as automatic emergency braking and forward collision warning.
To gain a better insight into the concepts of Ego-Lane and CIPV, check out our informative video that explains how these concepts look from a perception system's point-of-view. It is an excellent opportunity to learn more about these crucial aspects of ADAS and Autonomous Driving.
If you're interested in exploring more about the world of Perception and ADAS, check out this interactive tool - https://hubs.li/Q01F72js0
#perception #ADAS #autonomousdriving
Remaining time 1:18
Steve10
Regular
I think this product will benefit with Akida.
www.biped.ai
Avoid obstacles.
biped is the future of mobility for blind and visually impaired people. A smart harness, worn on shoulders, that uses self-driving technology from Honda Research Institute to guide people with short sounds. Ideal complement to a white cane or to a guide dog.
270 million visually impaired people worldwide walk with a risk of hitting obstacles, such as electric scooters, bicycles, or tree branches.
White canes alone cannot detect all obstacles. But replacing it is not the way to go. What if you could walk with your mind free, knowing where potential obstacles are? And get GPS instructions too?
It uses wide-angle cameras and AI to generate short sounds to warn you about the position of important obstacles, such as branches, holes, vehicles or pedestrians. It also provides GPS instructions (coming soon). Sounds are played in Bluetooth headphones.
biped is a harness worn on the shoulders, equipped with ultra-wide angle cameras on the left of your chest, a battery behind your neck, and a small computer on the right of your chest. It works just like a self-driving car, for pedestrians. We partnered with Honda Research Institute to bring the best of car research, to biped.
NOA by biped.ai, Live AI mobility companion
NOA is a smart AI-powered vest, worn on the shoulders. The best complement for white canes and guide dogs. Designed for and with blind users.
Avoid obstacles.
GPS navigation.
All-in-one.
biped is the future of mobility for blind and visually impaired people. A smart harness, worn on shoulders, that uses self-driving technology from Honda Research Institute to guide people with short sounds. Ideal complement to a white cane or to a guide dog.270 million visually impaired people worldwide walk with a risk of hitting obstacles, such as electric scooters, bicycles, or tree branches.
White canes alone cannot detect all obstacles. But replacing it is not the way to go. What if you could walk with your mind free, knowing where potential obstacles are? And get GPS instructions too?
It uses wide-angle cameras and AI to generate short sounds to warn you about the position of important obstacles, such as branches, holes, vehicles or pedestrians. It also provides GPS instructions (coming soon). Sounds are played in Bluetooth headphones.
biped is a harness worn on the shoulders, equipped with ultra-wide angle cameras on the left of your chest, a battery behind your neck, and a small computer on the right of your chest. It works just like a self-driving car, for pedestrians. We partnered with Honda Research Institute to bring the best of car research, to biped.
buena suerte :-)
BOB Bank of Brainchip
Certainly looking a lot healthier 
719 buyers for 12,393,513 units
377 sellers for 5,624,129 units
719 buyers for 12,393,513 units
377 sellers for 5,624,129 units
White Horse
Regular
Just in case some missed the second article from Forbes yesterday, here it is.
Karl Freund
Contributor
Founder and Principal Analyst, Cambrian-AI Research LLC
Mar 6, 2023
The company’s event-based digital Neuromorphic IP can add efficient AI processing to SoCs.
Edge AI is becoming a thing. Instead of using just an embedded microprocessor in edge applications and sending the data to a cloud for AI processing, many edge companies are considering adding AI at the edge itself, and then communicating conclusions about what the edge processor is “seeing” instead of sending the raw sensory data such as an image. To date, this dynamic has been held back by the cost and power requirements of initial implementations. What customers are looking for is proven AI tech that can run under a watt, and that they can add to a microcontroller for on-board processing.
Many startups have entered the field, looking to compete in an area of AI which does not have an 800-pound incumbent to displace (a.k.a., NVIDIA). Many startups have some sort of in-memory or near-memory architecture to reduce data movement, coupled with digital multiply-accumulate (MAC) logic. BrainChip is taking a different approach, applying event-base digital neuromorphic logic with SRAM that the company says is power-efficient, flexible, scalable, and enables on-chip learning. Let’s take a closer look.
Brainchip has a lengthy list of enhancements it has engineered to the second-generation Akida platform. The motivation of these additions has been to enable processing on the modalities customers are increasingly demanding: real-time video, audio, and time-series data such as speech recognition, human action recognition, text translation, and video object detection. For some of these apps, the addition of an optional Vision Transformer (ViT) engine, along with an enhanced neural mesh can deliver up to 50 TOPS (Trillions of Operations Per Second) according to the company.
The company is selling its IP into designs for sensors and SoCs looking to add AI to the edge. While uptake has been slow for BrainChip’s first product, the AKD1000, there have been some high-profile demonstrations of its use by companies like Mercedes in the EQXX concept vehicle and by NASA on a project for autonomy and cognition in small satellites and adoption of their development kits and boards by a number of companies for prototyping purposes.
Now, with the second generation. BrainChip has added support for 8-bit weights and activations, the ViT mentioned above, and hardware support for an innovative Temporal Event-Based Neural Net (TENN) support. Akida maintains its ability to process multiple layers at a time, managed by its smart DMA which handles model and data load and store autonomously. This can enable low-power sensors attached to an Akida node without the need for CPU. The diagram below shows how Akida sensors can be coupled with an SoC for multi-sensor inference processing.
[IMG alt="The Akida IP can be used to create sensors and be
deployed within more advanced SoCs."]https://imageio.forbes.com/specials...ed-SoCs-/960x0.jpg?format=jpg&width=960[/IMG]
The Akida IP can be used to create sensors and be deployed within more advanced SoCs.
BrainChip
The new Akida platform, expected to be available later this year. is designed to process a variety of popular networks, including CNNs, DNNs, Vision Transformers, and SNNs. The event-based design is particularly good at time series data for problems such as audio processing, video object detection, and vital sign monitoring and prediction.
BrainChip has shown some initial benchmarks that demonstrate orders of magnitude fewer operations and smaller model size which can benefit edge AI implementations. In video object detection, a 16m implementation can handle 30FPS at 1382x512 resolution, in under 75mW. Keyword detection in 28nm can support over 125 inferences/sec taking less than 2 microJoules per inference. BrainChip has applied for patents in TENN model acceleration.
[IMG alt="The Akida processor and software can implement
multi-pass processing as well as on-chip learning."]https://imageio.forbes.com/specials...-well-as/960x0.jpg?format=jpg&width=960[/IMG]
The Akida processor and software can implement multi-pass processing as well as on-chip learning.
BrainChip
Akida’s runtime software manages the operation efficiently including key features like its multi-pass processing which are handled transparent to the user. Model development and tuning is supported on the TensorFlow framework with MetaTF.
The Akida Architecture and associated Benefits.
BrainChip
BrainChip envisions three ranges of adoption, including a basic MCU with 1-4 nodes for always-on CPU-less operation, a MCU+Deep Learning Accelerators class, and a high-end MCU with up to 64 nodes and an optional ViT processor. In all cases, on-chip learning is possible by leveraging the trained model as a feature extractor and adding new classes in the final layer while untethered from cloud training resources.
[IMG alt="Akida can provide power efficient AI alternatives
across a broad range of solutions that currently
implement MCUs and GPUs."]https://imageio.forbes.com/specials...olutions/960x0.jpg?format=jpg&width=960[/IMG]
Akida can provide power efficient AI alternatives across a broad range of solutions that currently ... [+]
BrainChip
While BrainChip has been vocal in the past about the partnerships it has forged, such as with MegaChips and Renesas, commercial growth has been slower, possibly a function of the IP model taking longer to ramp. With the inclusion of 8-bit operations, the Vision Transformer, temporal convolutions (TENN models), and the ability to run models like ResNet-50 completely on the Akida Neural processor with minimal CPU dependencies, we believe the company is laying the foundation to turn the corner and land some bigger design wins. A key factor may be the software, which is currently TensorFlow based but will soon support PyTorch as well, an essential addition given the current development landscape.
BrainChip Readies 2nd Gen Platform For Power-Efficient Edge AI
Karl Freund
Contributor
Founder and Principal Analyst, Cambrian-AI Research LLC
Mar 6, 2023
The company’s event-based digital Neuromorphic IP can add efficient AI processing to SoCs.
Edge AI is becoming a thing. Instead of using just an embedded microprocessor in edge applications and sending the data to a cloud for AI processing, many edge companies are considering adding AI at the edge itself, and then communicating conclusions about what the edge processor is “seeing” instead of sending the raw sensory data such as an image. To date, this dynamic has been held back by the cost and power requirements of initial implementations. What customers are looking for is proven AI tech that can run under a watt, and that they can add to a microcontroller for on-board processing.
Many startups have entered the field, looking to compete in an area of AI which does not have an 800-pound incumbent to displace (a.k.a., NVIDIA). Many startups have some sort of in-memory or near-memory architecture to reduce data movement, coupled with digital multiply-accumulate (MAC) logic. BrainChip is taking a different approach, applying event-base digital neuromorphic logic with SRAM that the company says is power-efficient, flexible, scalable, and enables on-chip learning. Let’s take a closer look.
The Akida Platform
Brainchip has a lengthy list of enhancements it has engineered to the second-generation Akida platform. The motivation of these additions has been to enable processing on the modalities customers are increasingly demanding: real-time video, audio, and time-series data such as speech recognition, human action recognition, text translation, and video object detection. For some of these apps, the addition of an optional Vision Transformer (ViT) engine, along with an enhanced neural mesh can deliver up to 50 TOPS (Trillions of Operations Per Second) according to the company.
The company is selling its IP into designs for sensors and SoCs looking to add AI to the edge. While uptake has been slow for BrainChip’s first product, the AKD1000, there have been some high-profile demonstrations of its use by companies like Mercedes in the EQXX concept vehicle and by NASA on a project for autonomy and cognition in small satellites and adoption of their development kits and boards by a number of companies for prototyping purposes.
Now, with the second generation. BrainChip has added support for 8-bit weights and activations, the ViT mentioned above, and hardware support for an innovative Temporal Event-Based Neural Net (TENN) support. Akida maintains its ability to process multiple layers at a time, managed by its smart DMA which handles model and data load and store autonomously. This can enable low-power sensors attached to an Akida node without the need for CPU. The diagram below shows how Akida sensors can be coupled with an SoC for multi-sensor inference processing.
[IMG alt="The Akida IP can be used to create sensors and be
deployed within more advanced SoCs."]https://imageio.forbes.com/specials...ed-SoCs-/960x0.jpg?format=jpg&width=960[/IMG]
The Akida IP can be used to create sensors and be deployed within more advanced SoCs.
BrainChip
The new Akida platform, expected to be available later this year. is designed to process a variety of popular networks, including CNNs, DNNs, Vision Transformers, and SNNs. The event-based design is particularly good at time series data for problems such as audio processing, video object detection, and vital sign monitoring and prediction.
BrainChip has shown some initial benchmarks that demonstrate orders of magnitude fewer operations and smaller model size which can benefit edge AI implementations. In video object detection, a 16m implementation can handle 30FPS at 1382x512 resolution, in under 75mW. Keyword detection in 28nm can support over 125 inferences/sec taking less than 2 microJoules per inference. BrainChip has applied for patents in TENN model acceleration.
[IMG alt="The Akida processor and software can implement
multi-pass processing as well as on-chip learning."]https://imageio.forbes.com/specials...-well-as/960x0.jpg?format=jpg&width=960[/IMG]
The Akida processor and software can implement multi-pass processing as well as on-chip learning.
BrainChip
Akida’s runtime software manages the operation efficiently including key features like its multi-pass processing which are handled transparent to the user. Model development and tuning is supported on the TensorFlow framework with MetaTF.
The Akida Architecture and associated Benefits.
BrainChip
BrainChip envisions three ranges of adoption, including a basic MCU with 1-4 nodes for always-on CPU-less operation, a MCU+Deep Learning Accelerators class, and a high-end MCU with up to 64 nodes and an optional ViT processor. In all cases, on-chip learning is possible by leveraging the trained model as a feature extractor and adding new classes in the final layer while untethered from cloud training resources.
[IMG alt="Akida can provide power efficient AI alternatives
across a broad range of solutions that currently
implement MCUs and GPUs."]https://imageio.forbes.com/specials...olutions/960x0.jpg?format=jpg&width=960[/IMG]
Akida can provide power efficient AI alternatives across a broad range of solutions that currently ... [+]
BrainChip
Conclusions
While BrainChip has been vocal in the past about the partnerships it has forged, such as with MegaChips and Renesas, commercial growth has been slower, possibly a function of the IP model taking longer to ramp. With the inclusion of 8-bit operations, the Vision Transformer, temporal convolutions (TENN models), and the ability to run models like ResNet-50 completely on the Akida Neural processor with minimal CPU dependencies, we believe the company is laying the foundation to turn the corner and land some bigger design wins. A key factor may be the software, which is currently TensorFlow based but will soon support PyTorch as well, an essential addition given the current development landscape.
Falling Knife
Regular
So cool that our partners feel the need to increase their workforce to target or maximise potential from our new enhanced Akida range.Jobs ads for Sales Executives in USA & Europe for Edge Impulse targeting health monitoring market.
Proven experience with identifying and onboarding enterprise customers in the health monitoring arena.
Edge Impulse - Senior Enterprise Sales Executive - SaaS (EU)
Do you want to accelerate your SaaS sales career? With Edge Impulse, modern health wearables observe and analyze the human body to deliver new experiences from untapped product data. Edge Impulse accelerates the development of predictive maintenance algorithms using sensor, audio and vision data...jobs.lever.co
Edge Impulse - Senior Enterprise Sales Executive - SaaS (US)
Do you want to accelerate your SaaS sales career? With Edge Impulse, modern health wearables observe and analyze the human body to deliver new experiences from untapped product data. Edge Impulse accelerates the development of predictive maintenance algorithms using sensor, audio and vision data...jobs.lever.co
Bit of 'the chicken and the egg' scenario here:
Was Edge Impulse involved in the feedback loop with their customers having them already lined up for the change?
Or did this second generation enhancement trigger the mass market requiring the new sales FTE.
Either way one of OUR Ecosystem partners are preparing to manage the increase in demand which will help Sean execute his last statement from the txt above
"we are focused on executing more IP licenced agreements and generating revenue growth over the coming years."
Our team has support to assist our ubiquitous ambitions.
I am a patient person but I'd be so pleased to see ONE license land before May 23rd.
Will this last BRN announcement be the final key to unlocking the licensing charge. Was it the final proof that Akida can evolve and extend its reach into the future.
White Horse
Regular
From the fishmonger and baker of fine bread.
Peter Van Der Made
Chief Technology Officer at BrainChip
1 article
March 8, 2023
Introduces Vision Transformers and Spatial-Temporal Convolution for radically fast, hyper-efficient, and secure Edge AIoT products, untethered from the cloud
Laguna Hills, Calif. – March 6, 2023 – BrainChip Holdings Ltd (ASX: BRN, OTCQX: BRCHF, ADR: BCHPY), the world’s first commercial producer of ultra-low power, fully digital, neuromorphic AI IP, today announced the second generation of its Akida™ platform that drives extremely efficient and intelligent edge devices for the Artificial Intelligence of Things (AIoT) solutions and services market that is expected to be $1T+ by 2030. This hyper-efficient yet powerful neural processing system, architected for embedded Edge AI applications, now adds efficient 8-bit processing to go with advanced capabilities such as time domain convolutions and vision transformer acceleration, for an unprecedented level of performance in sub-watt devices, taking them from perception towards cognition.
The second-generation of Akida now includes Temporal Event Based Neural Nets (TENN) spatial-temporal convolutions that supercharge the processing of raw time-continuous streaming data, such as video analytics, target tracking, audio classification, analysis of MRI and CT scans for vital signs prediction, and time series analytics used in forecasting, and predictive maintenance. These capabilities are critically needed in industrial, automotive, digital health, smart home and smart city applications. The TENNs allow for radically simpler implementations by consuming raw data directly from sensors – drastically reduces model size and operations performed, while maintaining very high accuracy. This can shrink design cycles and dramatically lower the cost of development.
Another addition to the second generation of Akida is Vision Transformers (ViT) acceleration, a leading edge neural network that has been shown to perform extremely well on various computer vision tasks, such as image classification, object detection, and semantic segmentation. This powerful acceleration, combined with Akida’s ability to process multiple layers simultaneously and hardware support for skip connections, allows it to self-manage the execution of complex networks like RESNET-50 completely in the neural processor without CPU intervention and minimizes system load.
The Akida IP platform has a unique ability to learn on the device for continuous improvement and data-less customization that improves security and privacy. This, combined with the efficiency and performance available, enable very differentiated solutions that until now have not been possible. These include secure, small form factor devices like hearable and wearable devices, that take raw audio input, medical devices for monitoring heart and respiratory rates and other vitals that consume only microwatts of power. This can scale up to HD-resolution vision solutions delivered through high-value, battery-operated or fanless devices enabling a wide variety of applications from surveillance systems to factory management and augmented reality to scale effectively.
“We see an increasing demand for real-time, on-device, intelligence in AI applications powered by our MCUs and the need to make sensors smarter for industrial and IoT devices,” said Roger Wendelken, Senior Vice President in Renesas’ IoT and Infrastructure Business Unit. “We licensed Akida neural processors because of their unique neuromorphic approach to bring hyper-efficient acceleration for today’s mainstream AI models at the edge. With the addition of advanced temporal convolution and vision transformers, we can see how low-power MCUs can revolutionize vision, perception, and predictive applications in wide variety of markets like industrial and consumer IoT and personalized healthcare, just to name a few.”
“Advancements in AI require parallel advancements in on-device learning capabilities while simultaneously overcoming the challenges of efficiency, scalability, and latency,” said Richard Wawrzyniak, principal analyst at Semico Research. “BrainChip has demonstrated the ability to create a truly intelligent edge with Akida and moves the needle even more in terms of how Edge AI solutions are developed and deployed. The benefits of on-chip AI from a performance and cost perspective are hard to deny.”
“Our customers wanted us to enable expanded predictive intelligence, target tracking, object detection, scene segmentation, and advanced vision capabilities. This new generation of Akida allows designers and developers to do things that were not possible before in a low-power edge device,” said Sean Hehir, BrainChip CEO. “By inferring and learning from raw sensor data, removing the need for digital signal pre-processing, we take a substantial step toward providing a cloudless Edge AI experience.”
Akida’s software and tooling further simplifies the development and deployment of solutions and services with these features:
Akida comes with a Models Zoo and a burgeoning ecosystem of software, tools, and model vendors, as well as IP, SoC, foundry and system integrator partners. BrainChip is engaged with early adopters on the second generation IP platform. General availability will follow in Q3’ 2023.
Luca Verre, Co-Founder and CEO, Prophesee
Suk Lee, VP Design Ecosystem Development, Intel Foundry Services
Zach Shelby, Co-Founder and CEO, Edge Impulse
Daniel Mandell, Director, VDC Research
John Shanton, CEO, Ipsolon Research
Phil Dworsky, Global Head of Strategic Alliances, SiFive
Bhasker Rao, Founder, Ai Labs
Roger Wendelken, Senior Vice President IoT and Infrastructure Business Unit, Renesas
Nalin Balan, Head of Business Development, Reality.ai, a Renesas Company
Richard Wawrzyniak, Principal Analyst, Semico Research
Anup Das, Associate Professor, Drexel University
Sean Hehir, CEO, BrainChip
About BrainChip Holdings Ltd (ASX: BRN, OTCQX: BRCHF, ADR: BCHPY)
BrainChip is the worldwide leader in edge AI on-chip processing and learning. The company’s first-to-market, fully digital, event-based AI processor, AkidaTM, uses neuromorphic principles to mimic the human brain, analyzing only essential sensor inputs at the point of acquisition, processing data with unparalleled efficiency, precision, and economy of energy. Akida uniquely enables edge learning local to the chip, independent of the cloud, dramatically reducing latency while improving privacy and data security. Akida Neural processor IP, which can be integrated into SoCs on any process technology, has shown substantial benefits on today’s workloads and networks, and offers a platform for developers to create, tune and run their models using standard AI workflows like TensorFlow/Keras. In enabling effective edge compute to be universally deployable across real world applications such as connected cars, consumer electronics, and industrial IoT, BrainChip is proving that on-chip AI, close to the sensor, is the future, for its customers’ products, as well as the planet. Explore the benefits of Essential AI at www.brainchip.com.
Follow BrainChip on Twitter: https://www.twitter.com/BrainChip_inc
Follow BrainChip on LinkedIn: https://www.linkedin.com/company/7792006
BrainChip Introduces Second-Generation Akida Platform
Peter Van Der Made
Chief Technology Officer at BrainChip
1 article
March 8, 2023
Introduces Vision Transformers and Spatial-Temporal Convolution for radically fast, hyper-efficient, and secure Edge AIoT products, untethered from the cloud
Laguna Hills, Calif. – March 6, 2023 – BrainChip Holdings Ltd (ASX: BRN, OTCQX: BRCHF, ADR: BCHPY), the world’s first commercial producer of ultra-low power, fully digital, neuromorphic AI IP, today announced the second generation of its Akida™ platform that drives extremely efficient and intelligent edge devices for the Artificial Intelligence of Things (AIoT) solutions and services market that is expected to be $1T+ by 2030. This hyper-efficient yet powerful neural processing system, architected for embedded Edge AI applications, now adds efficient 8-bit processing to go with advanced capabilities such as time domain convolutions and vision transformer acceleration, for an unprecedented level of performance in sub-watt devices, taking them from perception towards cognition.
The second-generation of Akida now includes Temporal Event Based Neural Nets (TENN) spatial-temporal convolutions that supercharge the processing of raw time-continuous streaming data, such as video analytics, target tracking, audio classification, analysis of MRI and CT scans for vital signs prediction, and time series analytics used in forecasting, and predictive maintenance. These capabilities are critically needed in industrial, automotive, digital health, smart home and smart city applications. The TENNs allow for radically simpler implementations by consuming raw data directly from sensors – drastically reduces model size and operations performed, while maintaining very high accuracy. This can shrink design cycles and dramatically lower the cost of development.
Another addition to the second generation of Akida is Vision Transformers (ViT) acceleration, a leading edge neural network that has been shown to perform extremely well on various computer vision tasks, such as image classification, object detection, and semantic segmentation. This powerful acceleration, combined with Akida’s ability to process multiple layers simultaneously and hardware support for skip connections, allows it to self-manage the execution of complex networks like RESNET-50 completely in the neural processor without CPU intervention and minimizes system load.
The Akida IP platform has a unique ability to learn on the device for continuous improvement and data-less customization that improves security and privacy. This, combined with the efficiency and performance available, enable very differentiated solutions that until now have not been possible. These include secure, small form factor devices like hearable and wearable devices, that take raw audio input, medical devices for monitoring heart and respiratory rates and other vitals that consume only microwatts of power. This can scale up to HD-resolution vision solutions delivered through high-value, battery-operated or fanless devices enabling a wide variety of applications from surveillance systems to factory management and augmented reality to scale effectively.
“We see an increasing demand for real-time, on-device, intelligence in AI applications powered by our MCUs and the need to make sensors smarter for industrial and IoT devices,” said Roger Wendelken, Senior Vice President in Renesas’ IoT and Infrastructure Business Unit. “We licensed Akida neural processors because of their unique neuromorphic approach to bring hyper-efficient acceleration for today’s mainstream AI models at the edge. With the addition of advanced temporal convolution and vision transformers, we can see how low-power MCUs can revolutionize vision, perception, and predictive applications in wide variety of markets like industrial and consumer IoT and personalized healthcare, just to name a few.”
“Advancements in AI require parallel advancements in on-device learning capabilities while simultaneously overcoming the challenges of efficiency, scalability, and latency,” said Richard Wawrzyniak, principal analyst at Semico Research. “BrainChip has demonstrated the ability to create a truly intelligent edge with Akida and moves the needle even more in terms of how Edge AI solutions are developed and deployed. The benefits of on-chip AI from a performance and cost perspective are hard to deny.”
“Our customers wanted us to enable expanded predictive intelligence, target tracking, object detection, scene segmentation, and advanced vision capabilities. This new generation of Akida allows designers and developers to do things that were not possible before in a low-power edge device,” said Sean Hehir, BrainChip CEO. “By inferring and learning from raw sensor data, removing the need for digital signal pre-processing, we take a substantial step toward providing a cloudless Edge AI experience.”
Akida’s software and tooling further simplifies the development and deployment of solutions and services with these features:
- An efficient runtime engine that autonomously manages model accelerations completely transparent to the developer
- MetaTF™ software that developers can use with their preferred framework, like TensorFlow/Keras, or development platform, like Edge Impulse, to easily develop, tune, and deploy AI solutions.
- Supports all types of Convolutional Neural Networks (CNN), Deep Learning Networks (DNN), Vision Transformer Networks (ViT) as well as Spiking Neural Networks (SNNs), future-proofing designs as the models get more advanced.
Akida comes with a Models Zoo and a burgeoning ecosystem of software, tools, and model vendors, as well as IP, SoC, foundry and system integrator partners. BrainChip is engaged with early adopters on the second generation IP platform. General availability will follow in Q3’ 2023.
See what they’re saying:
Luca Verre, Co-Founder and CEO, Prophesee
Suk Lee, VP Design Ecosystem Development, Intel Foundry Services
Zach Shelby, Co-Founder and CEO, Edge Impulse
Daniel Mandell, Director, VDC Research
John Shanton, CEO, Ipsolon Research
Phil Dworsky, Global Head of Strategic Alliances, SiFive
Bhasker Rao, Founder, Ai Labs
Roger Wendelken, Senior Vice President IoT and Infrastructure Business Unit, Renesas
Nalin Balan, Head of Business Development, Reality.ai, a Renesas Company
Richard Wawrzyniak, Principal Analyst, Semico Research
Anup Das, Associate Professor, Drexel University
Sean Hehir, CEO, BrainChip
About BrainChip Holdings Ltd (ASX: BRN, OTCQX: BRCHF, ADR: BCHPY)
BrainChip is the worldwide leader in edge AI on-chip processing and learning. The company’s first-to-market, fully digital, event-based AI processor, AkidaTM, uses neuromorphic principles to mimic the human brain, analyzing only essential sensor inputs at the point of acquisition, processing data with unparalleled efficiency, precision, and economy of energy. Akida uniquely enables edge learning local to the chip, independent of the cloud, dramatically reducing latency while improving privacy and data security. Akida Neural processor IP, which can be integrated into SoCs on any process technology, has shown substantial benefits on today’s workloads and networks, and offers a platform for developers to create, tune and run their models using standard AI workflows like TensorFlow/Keras. In enabling effective edge compute to be universally deployable across real world applications such as connected cars, consumer electronics, and industrial IoT, BrainChip is proving that on-chip AI, close to the sensor, is the future, for its customers’ products, as well as the planet. Explore the benefits of Essential AI at www.brainchip.com.
Follow BrainChip on Twitter: https://www.twitter.com/BrainChip_inc
Follow BrainChip on LinkedIn: https://www.linkedin.com/company/7792006
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