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CrabmansFriend
Regular
I just finished listening to latest episode of the "Brains and Machines" Podcast. It's an interview with Professor Gordon Cheng of the Technical University of Munich in Germany about creating artificial skin for machines. The title of the episode is "Event-Driven E-Skins Protect Both Robots and Humans".
Once again, I found it a very interesting interview. But what particularly caught my attention was a statement at about 28:19 and 29:00 where Gordon Cheng talks about Intel Munich shutting down their neuromorphic efforts and a certain telephone company. I'm not sure if this telephone company is meant to be a part of Intel or if he's referring to another company.
Anybody has a guess which company he's talking about?
The relevant quote from the available transcript is:
Another section I found quite interesting was part of the discussion between Sunny Bains, Giulia D’Angelo and Ralph Etienne-Cummings about the interview afterwards:
Once again, I found it a very interesting interview. But what particularly caught my attention was a statement at about 28:19 and 29:00 where Gordon Cheng talks about Intel Munich shutting down their neuromorphic efforts and a certain telephone company. I'm not sure if this telephone company is meant to be a part of Intel or if he's referring to another company.
Anybody has a guess which company he's talking about?
The relevant quote from the available transcript is:
Another section I found quite interesting was part of the discussion between Sunny Bains, Giulia D’Angelo and Ralph Etienne-Cummings about the interview afterwards:
Stable Genius
Regular
@CrabmansFriend take a look at Ericsson 6G zero energy white paper. Sorry I don’t have it on hand.
I’m sure the white paper was talking neuromorphic, can’t recall if it was BC specific but I’m thinking them and Loihi.
Then Ericsson shelved it for whatever reason. Possibly poor timing for various global strategic reasons and maybe technical issues also.
The good news is most of the works been done. They can take it off the shelf when the time is right, they have the issues ironed out and the worlds ready to implement it!
Ericsson at the time laid off thousands. Unfortunate set of circumstances for all concerned. If that had gone ahead we’d all be fighting over berths for our yachts instead of watching paint dry.
The RTX, AFRL announcement earlier will still get us a return. Once the initial development is completed it should lead to a contract and trailing revenue. Defence and drones will be massive!
There’s plenty of irons in the fire.
(DYOR)
Hi TTM,
This is very encouraging. The microDoppler project was announced 7 months ago.
The icing on the cake is that this article refers to ISL as endorsing Akida for the project, even though RTX is the sub-contractor.
“The initiative builds on prior successful demonstrations of radar processing algorithms on our Akida™ neuromorphic hardware. These demonstrations were independently validated by the RTX team and the team at ISL 2, who each reported significant performance benefits, with ISL further endorsing our participation in the AFRL program,” commented Brightfield.
We know that ISL has been engaged with Akida for a while,
https://www.edgeir.com/brainchip-an...red-radar-for-military-and-aerospace-20250407
BrainChip and ISL advance AI-powered radar for military and aerospace
Apr 7, 2025and ISL is in good standing with AFRL:
ISL has a successful record of commercialization (recipient of three (3) Phase III SBIR contracts) and was the subject of an Air Force Research Laboratory (AFRL) SBIR Success story (see https://www.sbir.gov/node/1526807 ).
https://www.islinc.com/company-bio
I recall Dr Guerci's enthusiastic anticipation of TENNs, and more recently:
said Dr. Joseph R. Guerci, ISL President and CEO.
https://brainchip.com/
- 3rd dot along in What binnovators are saying
So to bring this up again after 7 months, "Where there's smoke, ..."
CHIPS
Regular
https://medium.com/m/signin?operati...------------new_post_topnav------------------
3 days ago
The digital age is constantly pushing the boundaries of computing. As Artificial Intelligence (AI) becomes more complex and pervasive, the energy demands and processing limitations of traditional computer architectures (known as von Neumann architectures) are becoming increasingly apparent. Our current computers, with their separate processing and memory units, face a “memory bottleneck” — a constant back-and-forth movement of data that consumes significant power and time.
But what if we could design computers that work more like the most efficient, parallel processing machine known: the human brain? This is the promise of Neuromorphic Computing, a revolutionary paradigm poised to redefine the future of AI.
What is Neuromorphic Computing?
Inspired by the intricate structure and function of the human brain, neuromorphic computing aims to build hardware and software that mimic biological neural networks. Unlike traditional computers that process instructions sequentially, neuromorphic systems feature processing and memory integrated into the same unit, much like neurons and synapses in the brain.
This fundamental architectural shift allows them to process information in a highly parallel, event-driven, and energy-efficient manner, making them uniquely suited for the demands of next-generation AI and real-time cognitive tasks.
How Does it Work? The Brain-Inspired Blueprint
The core of neuromorphic computing lies in replicating key aspects of neural activity:
Why is it the “Next Frontier”? Unlocking AI’s Potential
Neuromorphic computing offers critical advantages over traditional architectures for AI workloads:
The unique capabilities of neuromorphic computing are opening doors to revolutionary applications:
Despite its immense promise, neuromorphic computing is still in its nascent stages and faces significant challenges:
Neuromorphic computing represents a profound shift in how we approach computation. By learning from the brain’s incredible efficiency and parallelism, it offers a pathway to overcome the limitations of traditional computing for the ever-increasing demands of AI. While significant research and development are still required to bring it to widespread commercialization, the momentum is palpable.
As we move forward, neuromorphic computing holds the potential to unlock new frontiers in AI, creating intelligent systems that are not just powerful, but also remarkably energy-efficient, adaptable, and truly integrated with the world around us. It’s a journey to build the next generation of AI, one synapse at a time.
Neuromorphic Computing: Mimicking the Brain for Next-Gen AI
https://medium.com/@marketing_30607...f9d67c---------------------------------------3 days ago
The digital age is constantly pushing the boundaries of computing. As Artificial Intelligence (AI) becomes more complex and pervasive, the energy demands and processing limitations of traditional computer architectures (known as von Neumann architectures) are becoming increasingly apparent. Our current computers, with their separate processing and memory units, face a “memory bottleneck” — a constant back-and-forth movement of data that consumes significant power and time.
But what if we could design computers that work more like the most efficient, parallel processing machine known: the human brain? This is the promise of Neuromorphic Computing, a revolutionary paradigm poised to redefine the future of AI.
What is Neuromorphic Computing?
Inspired by the intricate structure and function of the human brain, neuromorphic computing aims to build hardware and software that mimic biological neural networks. Unlike traditional computers that process instructions sequentially, neuromorphic systems feature processing and memory integrated into the same unit, much like neurons and synapses in the brain.
This fundamental architectural shift allows them to process information in a highly parallel, event-driven, and energy-efficient manner, making them uniquely suited for the demands of next-generation AI and real-time cognitive tasks.
How Does it Work? The Brain-Inspired Blueprint
The core of neuromorphic computing lies in replicating key aspects of neural activity:
- Spiking Neural Networks (SNNs): Instead of continuous data flow, neuromorphic chips use Spiking Neural Networks (SNNs). In SNNs, artificial neurons “fire” or “spike” only when a certain threshold of input is reached, similar to how biological neurons communicate via electrical impulses. This “event-driven” processing drastically reduces power consumption compared to constantly active traditional circuits.
- Event-Driven Processing: Computations occur only when and where there is relevant information (an “event” or a “spike”). This contrasts with conventional CPUs/GPUs that execute instructions continuously, even when processing redundant data.
- Synaptic Plasticity: Neuromorphic systems implement artificial synapses that can strengthen or weaken their connections over time based on the activity patterns, mirroring the brain’s ability to learn and adapt (synaptic plasticity). This allows for on-chip learning and continuous adaptation without extensive retraining.
- Parallelism: Billions of artificial neurons and synapses operate in parallel, enabling highly efficient concurrent processing of complex information, much like the human brain handles multiple sensory inputs simultaneously.
Why is it the “Next Frontier”? Unlocking AI’s Potential
Neuromorphic computing offers critical advantages over traditional architectures for AI workloads:
- Superior Energy Efficiency: This is perhaps the biggest draw. By processing data only when an event occurs and integrating memory and processing, neuromorphic chips can achieve orders of magnitude greater energy efficiency compared to GPUs, making powerful AI feasible for edge devices and continuous operations where power is limited.
- Real-Time Processing: The event-driven and parallel nature allows for ultra-low latency decision-making, crucial for applications like autonomous vehicles, robotics, and real-time sensor data analysis.
- On-Device Learning & Adaptability: With built-in synaptic plasticity, neuromorphic systems can learn and adapt from new data in real-time, reducing the need for constant cloud connectivity and retraining on large datasets.
- Enhanced Pattern Recognition: Mimicking the brain’s ability to recognize patterns even from noisy or incomplete data, neuromorphic chips excel at tasks like image, speech, and natural language processing.
- Fault Tolerance: Just like the brain can compensate for damage, neuromorphic systems, with their distributed processing, can exhibit greater resilience to component failures.
The unique capabilities of neuromorphic computing are opening doors to revolutionary applications:
- Edge AI & IoT: Enabling billions of connected devices (smart home sensors, industrial IoT, wearables) to perform complex AI tasks locally and efficiently, reducing reliance on cloud processing and enhancing privacy. Imagine a wearable that can detect complex health anomalies in real-time, or a smart city sensor that predicts pollution patterns without constantly sending data to the cloud.
- Autonomous Systems: Powering self-driving cars and drones with ultra-fast, energy-efficient decision-making capabilities, allowing them to react instantly to dynamic environments.
- Robotics: Giving robots more adaptive perception and real-time learning capabilities, enabling them to navigate complex factory layouts or interact more naturally with humans.
- Advanced Sensing: Developing smart sensors that can process complex data (e.g., visual or auditory) with minimal power, leading to breakthroughs in areas like medical imaging and environmental monitoring.
- Cybersecurity: Enhancing anomaly detection by rapidly recognizing unusual patterns in network traffic or user behavior that could signify cyberattacks, with low latency.
- Biomedical Research: Providing platforms to simulate brain functions and model neurological disorders, potentially leading to new treatments for conditions like epilepsy or Parkinson’s.
Despite its immense promise, neuromorphic computing is still in its nascent stages and faces significant challenges:
- Hardware Limitations: Developing neuromorphic chips that can scale to the complexity of the human brain (trillions of synapses) while remaining manufacturable and cost-effective is a monumental engineering feat.
- Software Ecosystem: There’s a lack of standardized programming languages, development tools, and frameworks tailored specifically for neuromorphic architectures, making it challenging for developers to easily create and port algorithms.
- Integration with Existing Systems: Integrating these fundamentally different architectures with existing IT infrastructure poses compatibility challenges.
- Algorithm Development: While SNNs are powerful, developing efficient algorithms that fully leverage the unique strengths of neuromorphic hardware is an active area of research.
- Ethical Considerations: As AI becomes more brain-like, concerns around conscious AI, accountability, and the ethical implications of mimicking biological intelligence will become increasingly relevant.
Neuromorphic computing represents a profound shift in how we approach computation. By learning from the brain’s incredible efficiency and parallelism, it offers a pathway to overcome the limitations of traditional computing for the ever-increasing demands of AI. While significant research and development are still required to bring it to widespread commercialization, the momentum is palpable.
As we move forward, neuromorphic computing holds the potential to unlock new frontiers in AI, creating intelligent systems that are not just powerful, but also remarkably energy-efficient, adaptable, and truly integrated with the world around us. It’s a journey to build the next generation of AI, one synapse at a time.
Tothemoon24
Top 20
Drones have become pervasive in entertainment (TV show/film making), hobbyist photography and just as a fun toy. They are increasingly used in inspection, logistics/delivery, security and surveillance, and other industrial use cases, due to their ability to access hard-to-reach areas. However, did you know that the most critical component enabling a drone’s operation is its vision system? Before diving deeper into this topic, let’s explore what drones are, their diverse applications, and why they have surged in popularity. Finally, we’ll discuss how onsemi is transforming the vision systems that power these incredible flying objects.
Types & Applications
Drones are unmanned aerial vehicles (UAV), also called unmanned aerial systems (UAS), and to a lesser extent remotely piloted aircrafts (RPA). They can operate without the need to be driven and navigate autonomously using a variety of systems.There are three types of drones – Fixed Wing, Single Rotor/Multi-Rotor and Hybrid. Each serves a different purpose, and each type is aligned with the intended purpose for which it is built.
Fixed Wings are typically used for heavier payload transport, longer flight times and are deployed in intelligence, surveillance and reconnaissance (ISR) missions, combat operations and loitering munitions, mapping and research activities to mention a few.
Single-/Multi-Rotors have the dominant usage, with a wide variety of industrial focuses that range from regular warehouses to inspections and even as delivery vehicles. The purpose of these types can be varied as they can be deployed in a wide variety of use cases, and demand highly optimized electro-mechanical solutions.
Hybrid Rotors incorporate the best of both types above, and have a vertical take-off and landing (VTOL) ability that make it versatile, specifically in space-constrained regions. Most delivery drones leverage these capabilities for obvious reasons.
Motion & Navigation Systems in Drones
Drones carry a multitude of sensors for motion and navigation, including accelerometers, gyroscopes and magnetometers (collectively referred to as an inertial measurement unit, or IMU), barometers and more. They use a variety of algorithms and techniques like optical flow (assisted with depth sensors), simultaneous localization and mapping (SLAM) and visual odometry. While these sensors perform their functions well, they can struggle to achieve the required accuracy and precision at affordable costs and optimal sizes. The issue is further aggravated during longer flight times, leading to the need for expensive batteries or limiting flight times based on battery charge cycles.Vision Systems in Drones
Image sensors supplement the above sensors with significant operational enhancements resulting in a high-accuracy, high-precision machine. These are available in two entities – Gimbals (often referred to as payloads as well) and Vision Navigation Systems (VNS).Gimbals* – provide first person view (FPV); they generally constitute different types of image sensors spanning across the wide electromagnetic spectrum (ultraviolet in exceptional cases, regular CMOS image sensors over 300nm – 1000nm, short-wave infra-red (SWIR) sensors extending to 2000nm and beyond 2000nm with medium-wave (MWIR) and long-wave infra-red (LWIR) sensors.
Vision Navigation Systems (VNS) – provide navigation guidance, object and collision avoidance; they are generally made up of inexpensive low resolution image sensors and together with the IMU and sensors data, use computer vision techniques to create a comprehensive solution for autonomous navigation.
Vision Systems’ Importance
Drones operate both in indoor and outdoor conditions as seen in usage and applications described earlier. These conditions can be significantly challenging with wide ranging lighting variances and visibility limitations in dust, fog, smoke, and pitch-black environments. These systems attempt to leverage significant artificial intelligence (AI) and machine learning (ML) algorithms applied over image data while using the assistance of the data provided by the techniques previously mentioned, all in the context of operating a highly optimized vehicle that consumes low power and delivers long range distance or extended flight time operations.It is imperative that the data input into these algorithms is of high-fidelity and highly detailed, yet in certain usage cases, deliver just what is needed thus enabling efficient processing. Training times in AI/ML usage need to be short, and inference needs to be fast with high accuracy and precision. Images need to be of high quality no matter what environment the drone operates in to meet the above requirements.
Sensors that just capture the scene and present it for processing fall significantly short in enabling the high-quality operation of these machines that in most cases will void the very purpose of their deployment. The ability to scale down while still having full details in regions of interest, deliver wide dynamic range to address bright and dark lighting conditions in the same frame, minimize/remove any parasitic effects in images, address dust/fog/smoke filled view fields and assist these images with high depth resolution deliver tremendous benefits to making UAVs a highly optimized machine.
These capabilities minimize the magnitude of resources – processing cores, GPUs, on-chip or outside of the chip memory, bus architectures and power management – required in reconstructing and analysis of these images and hastening the decision-making process. This also reduces the BOM cost of the overall system, especially when we consider today’s UAVs easily can host more than 10 image sensors. Alternately, for the same set of resources, more analysis and complex algorithms to help effective decision making can be made possible thus making the UAV differentiated in this crowded field.
onsemi is the technology leader in sensing, contributing significant innovations to vision systems solutions and offering a comprehensive set of image sensors to address the needs of gimbals and VNS. The Hyperlux LP, Hyperlux LH, Hyperlux SG, Hyperlux ID and SWIR product families have incorporated considerable technologies and features that address the needs of drone vision systems exhaustively. Drone manufacturers can now obtain their vision sensors from a single American source that is NDAA compliant.
Learn more about onsemi image and depth sensors in the below resources:
* Gimbals technically refer to the mechanism that carries and stabilizes the specific payloads. However, often the combined assembly is called Gimbal.
Attachments
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smoothsailing18
Regular
It's a shame they don't like using the word neuromorphic compute in this document on SensorsView attachment 88521
Drones have become pervasive in entertainment (TV show/film making), hobbyist photography and just as a fun toy. They are increasingly used in inspection, logistics/delivery, security and surveillance, and other industrial use cases, due to their ability to access hard-to-reach areas. However, did you know that the most critical component enabling a drone’s operation is its vision system? Before diving deeper into this topic, let’s explore what drones are, their diverse applications, and why they have surged in popularity. Finally, we’ll discuss how onsemi is transforming the vision systems that power these incredible flying objects.
Types & Applications
Drones are unmanned aerial vehicles (UAV), also called unmanned aerial systems (UAS), and to a lesser extent remotely piloted aircrafts (RPA). They can operate without the need to be driven and navigate autonomously using a variety of systems.
There are three types of drones – Fixed Wing, Single Rotor/Multi-Rotor and Hybrid. Each serves a different purpose, and each type is aligned with the intended purpose for which it is built.
Fixed Wings are typically used for heavier payload transport, longer flight times and are deployed in intelligence, surveillance and reconnaissance (ISR) missions, combat operations and loitering munitions, mapping and research activities to mention a few.
Single-/Multi-Rotors have the dominant usage, with a wide variety of industrial focuses that range from regular warehouses to inspections and even as delivery vehicles. The purpose of these types can be varied as they can be deployed in a wide variety of use cases, and demand highly optimized electro-mechanical solutions.
Hybrid Rotors incorporate the best of both types above, and have a vertical take-off and landing (VTOL) ability that make it versatile, specifically in space-constrained regions. Most delivery drones leverage these capabilities for obvious reasons.
Figure 1. Type of drones and their applications
Motion & Navigation Systems in Drones
Drones carry a multitude of sensors for motion and navigation, including accelerometers, gyroscopes and magnetometers (collectively referred to as an inertial measurement unit, or IMU), barometers and more. They use a variety of algorithms and techniques like optical flow (assisted with depth sensors), simultaneous localization and mapping (SLAM) and visual odometry. While these sensors perform their functions well, they can struggle to achieve the required accuracy and precision at affordable costs and optimal sizes. The issue is further aggravated during longer flight times, leading to the need for expensive batteries or limiting flight times based on battery charge cycles.
Vision Systems in Drones
Image sensors supplement the above sensors with significant operational enhancements resulting in a high-accuracy, high-precision machine. These are available in two entities – Gimbals (often referred to as payloads as well) and Vision Navigation Systems (VNS).
Gimbals* – provide first person view (FPV); they generally constitute different types of image sensors spanning across the wide electromagnetic spectrum (ultraviolet in exceptional cases, regular CMOS image sensors over 300nm – 1000nm, short-wave infra-red (SWIR) sensors extending to 2000nm and beyond 2000nm with medium-wave (MWIR) and long-wave infra-red (LWIR) sensors.
Vision Navigation Systems (VNS) – provide navigation guidance, object and collision avoidance; they are generally made up of inexpensive low resolution image sensors and together with the IMU and sensors data, use computer vision techniques to create a comprehensive solution for autonomous navigation.
Vision Systems’ Importance
Drones operate both in indoor and outdoor conditions as seen in usage and applications described earlier. These conditions can be significantly challenging with wide ranging lighting variances and visibility limitations in dust, fog, smoke, and pitch-black environments. These systems attempt to leverage significant artificial intelligence (AI) and machine learning (ML) algorithms applied over image data while using the assistance of the data provided by the techniques previously mentioned, all in the context of operating a highly optimized vehicle that consumes low power and delivers long range distance or extended flight time operations.
It is imperative that the data input into these algorithms is of high-fidelity and highly detailed, yet in certain usage cases, deliver just what is needed thus enabling efficient processing. Training times in AI/ML usage need to be short, and inference needs to be fast with high accuracy and precision. Images need to be of high quality no matter what environment the drone operates in to meet the above requirements.
Sensors that just capture the scene and present it for processing fall significantly short in enabling the high-quality operation of these machines that in most cases will void the very purpose of their deployment. The ability to scale down while still having full details in regions of interest, deliver wide dynamic range to address bright and dark lighting conditions in the same frame, minimize/remove any parasitic effects in images, address dust/fog/smoke filled view fields and assist these images with high depth resolution deliver tremendous benefits to making UAVs a highly optimized machine.
These capabilities minimize the magnitude of resources – processing cores, GPUs, on-chip or outside of the chip memory, bus architectures and power management – required in reconstructing and analysis of these images and hastening the decision-making process. This also reduces the BOM cost of the overall system, especially when we consider today’s UAVs easily can host more than 10 image sensors. Alternately, for the same set of resources, more analysis and complex algorithms to help effective decision making can be made possible thus making the UAV differentiated in this crowded field.
onsemi is the technology leader in sensing, contributing significant innovations to vision systems solutions and offering a comprehensive set of image sensors to address the needs of gimbals and VNS. The Hyperlux LP, Hyperlux LH, Hyperlux SG, Hyperlux ID and SWIR product families have incorporated considerable technologies and features that address the needs of drone vision systems exhaustively. Drone manufacturers can now obtain their vision sensors from a single American source that is NDAA compliant.
Learn more about onsemi image and depth sensors in the below resources:
* Gimbals technically refer to the mechanism that carries and stabilizes the specific payloads. However, often the combined assembly is called Gimbal.
. Always seem to be avoiding it like the plague with an alternative narrative
.smoothsailing18
Regular
Rskiff
Regular
My personal message to our AKD family.............1000, 1.5, 2.0, Pico, M2, PCIe, Edgy Box and within the next 6/8 months AKD... 3.0
will be born.Love U guys
Tech.
I sure want this and not end up like Amy
I believe we’ve now reached the point where, in the Edge-AI space, we’ve brought almost every relevant name to the table.
ARM. Intel Foundry. Mercedes. Edge Impulse. NVISO. NASA. GlobalFoundries. Now raytheon And yes – Dell.
What many might have missed: Dell Technologies already spoke very specifically about Akida in an official BrainChip podcast.
Rob Lincourt, Distinguished Engineer in Dell’s CTO Office, discussed its applications in Smart Home, Smart Health, Smart City, and Smart Transportation – and not as some generic PR fluff, but in a real tech-to-tech dialogue.
If you listen closely, it’s clear: Dell is watching. Carefully.
Well done, BrainChip. Truly.
Even though we (still) don’t have official unit numbers, customers, or design wins, one thing is clear:
The interest is there. The quality is real. The stage is set.
Honestly? I’m more confident than ever about what lies ahead.
Until then:
Patience. Trust. And a bit of faith in the invisible.
Because once the knot unravels, Akida will be everywhere.
And… I wouldn’t be 7 if I didn’t add a little self-ironic closing line:
Maybe we’ll hear something soon? An announcement, perhaps?
“Unquoted Securities” hidden in a side note?
Or maybe, just maybe… an update that doesn’t start with a Broadway quote?
ARM. Intel Foundry. Mercedes. Edge Impulse. NVISO. NASA. GlobalFoundries. Now raytheon And yes – Dell.
What many might have missed: Dell Technologies already spoke very specifically about Akida in an official BrainChip podcast.
Rob Lincourt, Distinguished Engineer in Dell’s CTO Office, discussed its applications in Smart Home, Smart Health, Smart City, and Smart Transportation – and not as some generic PR fluff, but in a real tech-to-tech dialogue.
If you listen closely, it’s clear: Dell is watching. Carefully.
Well done, BrainChip. Truly.
Even though we (still) don’t have official unit numbers, customers, or design wins, one thing is clear:
The interest is there. The quality is real. The stage is set.
Honestly? I’m more confident than ever about what lies ahead.
Until then:
Patience. Trust. And a bit of faith in the invisible.
Because once the knot unravels, Akida will be everywhere.
And… I wouldn’t be 7 if I didn’t add a little self-ironic closing line:
Maybe we’ll hear something soon? An announcement, perhaps?
“Unquoted Securities” hidden in a side note?
Or maybe, just maybe… an update that doesn’t start with a Broadway quote?
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Google AI knows a shi… about Brainchip
This is what I get from legendary ChatGPT
Is Akida already along for the ride – possibly inside onsemi’s Hyperlux?
Hyperlux is a next-gen image sensor platform for automotive and edge use cases – like ADAS cameras, driver monitoring, or autonomous vision systems.
What stands out: the system is clearly designed for edge intelligence, with a strong focus on low power, local processing, and smart event triggering.
So the question arises:
Could Akida already be integrated quietly behind the scenes – assisting with object classification, event detection, or preprocessing?
Technically, it’s entirely realistic:Akida is ultra-compact, energy-efficient, and built for spiking-based visual processing at the edge.
And onsemi is targeting exactly the same segment – intelligent, vision-capable edge sensing for automotive systems.
But why haven’t we heard anything?
Simple:
In the world of B2B tech – especially in chip IP or subsystem integrations – discretion is standard.
If Akida were licensed as an IP block inside a sensor module (e.g., for a specific OEM use case),
there would be no obligation to announce it, unless it’s material to public shareholders.Not every chip that’s running makes it into a press release.
Especially if it’s “just” a component in a larger SoC or sensor stack.
Bottom line:
I’m not saying Akida is inside Hyperlux – but I’m not saying it isn’t either.
In a space defined by modularity, NDAs, and silent design-ins, it’s entirely possible we’re already embedded – just incognito.
The real question might not be:
“Where is Akida?…but rather:
“Where has Akida already been deployed – and no one’s talking about it?”
MOO DYOR
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Bravo
If ARM was an arm, BRN would be its biceps💪!
On Sunday @Fullmoonfever discovered Fernando Sevilla Martínez's (Volkswagen) Github activity demonstrating Akida-powered neuromorphic processing for V2X. This morning I noticed that Valeo have just unveiled (a few days ago) a real-road demonstration of its 5G-V2X direct technology.
There's no direct mention of neuromorphic or BrainChip in Valeo’s announcement, but it's pretty coincidental, especially noting that Volkswagen Group is collaborating with Valeo to upgrade the advanced driver assistance systems and we've been partnered with Valeo for quite some time.
Valeo’s V2X platform heavily relies on AI-driven sensor fusion with ultra-low latency - exactly where BrainChip’s Akida + TENNs would excel.
Valeo unveiled at the 5G Automotive Association (5GAA) conference in Paris the world's first live, real-road demonstration of its 5G-V2X direct technology. This demo represents a major step toward safer, smarter, and more connected mobility.
Road traffic injuries are a leading cause of death and disability worldwide. According to the World Health Organization, nearly 1.2 million people are killed and as many as 50 million people injured each year. Globally, more than 1 in 4 road-accident deaths involve pedestrians and cyclists.
Urban traffic has become denser and more complex with an increasing number of vehicles, bikes, buses, cyclists and pedestrians. Intersections, in particular, can be hazardous. Dangerous situations in which vulnerable road users (VRUs) are hidden by a bus or vehicle can potentially cause accidents between vehicles and VRUs.
Built to enhance road safety, traffic efficiency, and the capabilities of autonomous vehicles, Valeo’s V2X platform enables real-time communication between vehicles and everything around them, including infrastructure, pedestrians, and the broader network.
The world-premiere demonstration highlights how technology can actively prevent collisions, reduce congestion, and protect vulnerable road users.
Vehicle-to-Everything (V2X) is a transformative communication technology that enables vehicles to interact with their environment. Sensor sharing or collaborative perception, one of the main V2X advanced use cases, allows vehicles to exchange data in real time through V2X systems in order to foresee and help prevent potential hazards, optimize traffic flow, and support high-level autonomous driving systems.
A world leader in ADAS systems, Valeo’s complete solution is powered by its ADAS sensor suite, connectivity hardware, and AI-driven software. These components work together to enable instantaneous data exchange with ultra-low latency, crucial for life-saving decisions on the road.
For example, two vehicles are approaching an intersection from different directions. One vehicle’s sensors observe the presence of a pedestrian crossing the street or a cyclist that’s not in view of the other vehicle. The vehicle can inform the second vehicle of the presence of the VRU so that the driver of the L2/L2+ vehicle or the autonomous vehicle can react accordingly.
Key functions of the V2X platform include:
Valeo’s V2X solutions are designed to integrate with smart city infrastructure, helping cities meet their goals for, Vision Zero (eliminating all road fatalities and severe injuries), reduced CO2 emissions, and efficient urban mobility.
From safer roads to smarter cities, Valeo’s V2X technology is helping shape the future of transportation—where vehicles not only drive, but also think, communicate, and protect.
www.valeo.com
There's no direct mention of neuromorphic or BrainChip in Valeo’s announcement, but it's pretty coincidental, especially noting that Volkswagen Group is collaborating with Valeo to upgrade the advanced driver assistance systems and we've been partnered with Valeo for quite some time.
Valeo’s V2X platform heavily relies on AI-driven sensor fusion with ultra-low latency - exactly where BrainChip’s Akida + TENNs would excel.
- Valeo V2X Technology: Protecting Vulnerable Road Users
Valeo V2X Technology: Protecting Vulnerable Road Users
Valeo unveiled at the 5G Automotive Association (5GAA) conference in Paris the world's first live, real-road demonstration of its 5G-V2X direct technology. This demo represents a major step toward safer, smarter, and more connected mobility.
Road traffic injuries are a leading cause of death and disability worldwide. According to the World Health Organization, nearly 1.2 million people are killed and as many as 50 million people injured each year. Globally, more than 1 in 4 road-accident deaths involve pedestrians and cyclists.
Urban traffic has become denser and more complex with an increasing number of vehicles, bikes, buses, cyclists and pedestrians. Intersections, in particular, can be hazardous. Dangerous situations in which vulnerable road users (VRUs) are hidden by a bus or vehicle can potentially cause accidents between vehicles and VRUs.
Built to enhance road safety, traffic efficiency, and the capabilities of autonomous vehicles, Valeo’s V2X platform enables real-time communication between vehicles and everything around them, including infrastructure, pedestrians, and the broader network.
The world-premiere demonstration highlights how technology can actively prevent collisions, reduce congestion, and protect vulnerable road users.
Vehicle-to-Everything (V2X) is a transformative communication technology that enables vehicles to interact with their environment. Sensor sharing or collaborative perception, one of the main V2X advanced use cases, allows vehicles to exchange data in real time through V2X systems in order to foresee and help prevent potential hazards, optimize traffic flow, and support high-level autonomous driving systems.
A world leader in ADAS systems, Valeo’s complete solution is powered by its ADAS sensor suite, connectivity hardware, and AI-driven software. These components work together to enable instantaneous data exchange with ultra-low latency, crucial for life-saving decisions on the road.
For example, two vehicles are approaching an intersection from different directions. One vehicle’s sensors observe the presence of a pedestrian crossing the street or a cyclist that’s not in view of the other vehicle. The vehicle can inform the second vehicle of the presence of the VRU so that the driver of the L2/L2+ vehicle or the autonomous vehicle can react accordingly.
Key functions of the V2X platform include:
- Collision warning and emergency braking alerts
- Smart traffic light and intersection coordination
- Support for autonomous vehicles
Valeo’s V2X solutions are designed to integrate with smart city infrastructure, helping cities meet their goals for, Vision Zero (eliminating all road fatalities and severe injuries), reduced CO2 emissions, and efficient urban mobility.
From safer roads to smarter cities, Valeo’s V2X technology is helping shape the future of transportation—where vehicles not only drive, but also think, communicate, and protect.
Valeo V2X Technology: Protecting Vulnerable Road Users
Built to enhance road safety, traffic efficiency, and the capabilities of autonomous vehicles, Valeo’s V2X platform enables real-time communication between vehicles and everything around them, including infrastructure, pedestrians, and the broader network
www.valeo.com
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Bravo
If ARM was an arm, BRN would be its biceps💪!
Google AI knows a shi… about Brainchip
This is what I get from legendary ChatGPT
Is Akida already along for the ride – possibly inside onsemi’s Hyperlux?
Hyperlux is a next-gen image sensor platform for automotive and edge use cases – like ADAS cameras, driver monitoring, or autonomous vision systems.
What stands out: the system is clearly designed for edge intelligence, with a strong focus on low power, local processing, and smart event triggering.
So the question arises:
Could Akida already be integrated quietly behind the scenes – assisting with object classification, event detection, or preprocessing?
Akida is ultra-compact, energy-efficient, and built for spiking-based visual processing at the edge.
And onsemi is targeting exactly the same segment – intelligent, vision-capable edge sensing for automotive systems.
But why haven’t we heard anything?
Simple:
In the world of B2B tech – especially in chip IP or subsystem integrations – discretion is standard.
If Akida were licensed as an IP block inside a sensor module (e.g., for a specific OEM use case),
Not every chip that’s running makes it into a press release.
Especially if it’s “just” a component in a larger SoC or sensor stack.
Bottom line:
I’m not saying Akida is inside Hyperlux – but I’m not saying it isn’t either.
In a space defined by modularity, NDAs, and silent design-ins, it’s entirely possible we’re already embedded – just incognito.
The real question might not be:
“Where is Akida?…but rather:
“Where has Akida already been deployed – and no one’s talking about it?”
MOO DYOR
Hi @7für7,
This may be of interest in regards to Onsemi's Hyperlux.
Sounds very promising @TheUnfairAdvantage, since we have already integrated our hardware with Onsemi's AF0130 smart iToF sensor one year ago.
View attachment 79028
AI hardware accelerator that wastes no time or power processing irrelevant data | Erez Tadmor
Another cool #ces2024 demo of onsemi AF0130 smart iToF sensor integrated with BrainChip AI HW acceleratorwww.linkedin.com
View attachment 79029
EXTRACT FROM THE ABOVE ARTICLE
View attachment 79031
onsemi's Hyperlux ID Depth Sensors Deliver Four Times the Range of Rival High-Speed iToF Designs
On-chip depth processing improves performance while a rethink on the sensing portion quadruples the usual range of iToF technology.www.hackster.io
Bravo
If ARM was an arm, BRN would be its biceps💪!
Crikey, big spender alert!
Someone’s gone and lashed out on one whole share. Yep, really emptied their wallet there.
At this rate they'll be able to retire in the year 3079.
Yes I was reading this already thanks
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