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BRN Discussion Ongoing
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Frangipani
Top 20
Yet another BRN employee has left…
Our former ML Solutions Architect Dhvani Kothari is now an AI Solutions Architect with Contextual AI:
www.linkedin.com
www.linkedin.com
Our former ML Solutions Architect Dhvani Kothari is now an AI Solutions Architect with Contextual AI:
#contextualai #genai #techcareer | Dhvani Kothari | 39 comments
🎉 Excited to share that I've joined Contextual AI as a Solutions Architect! 🚀 The innovation and vision here are inspiring, and I’m eager to contribute to what’s next in enterprise AI. I want to take a moment to express my heartfelt gratitude to everyone at BrainChip for an incredible...
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That can be debated, he tries hard to be funny, often insulting people in the process.
Personally, I don't care, it's not his fault he is a clown and treat the forum like he is in the pub after work.
You have the right of being here just as everybody else, so just keep on posting and ignore the posters you don't like, you can put them on ignore and not see their posts anymore.
I think most here are tired of waiting, if they don't deliver before the next AGM, they are done for and can sell the company as far as I am concerned.
It's not acceptable they keep on printing shares for keeping the light on, hollowing out shareholders part of the company.
Sean said that he expect orders of 9 mill bucks this year, the window is closing, but I will give him time before I will get more harsh in my criticism.
Frangipani
Top 20
As much as I like your research Frangi, please show the same courtesy as you would expect from others on this forum, namely, I asked you a question a number of weeks ago, but you chose not to respond, now for someone whom likes to comment on other posters, for good or bad, can you give me and others your view on how you personally think Brainchip is tracking towards a successful outcome...going by the amount of time you spend in showcasing facts, you must have formed an opinion one way or another.
Be assured, Australians only Sue if you are caught out lying... appreciate an honest response this time !
Best regards....Texta
Hi @TECH,
so you asked me for an honest response to your question how I “personally think BrainChip is tracking towards a successful outcome”?
Voilà: Well, I believe the successful outcome that all of us genuine shareholders hope for will ultimately depend on an interplay of multiple factors, but primarily on the capability of our management to convert the apparently super-high customer interest (if we are to believe our CEO) into more actual sales.
My BrainChip journey over the past 3.5 years has convinced me that the tech is real and has been and continues to be validated by researchers in both academia and industry all over the world. At the same time, though, it has taught me that the adoption of neuromorphic technology is taking much longer than anticipated.
The amount of time I voluntarily dedicate to this forum and the content I contribute should give you a hint that I’m optimistic in principle, yet not uncritical of our company. I haven’t sold a single of my BRN shares - on the contrary, I’ve been accumulating ever since I first bought in. However, I would definitely not go as far as claim that BrainChip had reached its tipping point and that I personally no longer see any risk, like FF did back in March 2024. Just recall the financial predicament our partner Prophesee found themselves in about a year ago - and they were much further advanced on their commercialisation path than we currently are.
There’s a limit to what my and anyone else’s sleuthing and dot-joining can tell us about a future “successful outcome”.
After all, we are not privy to knowing what’s going on behind closed doors, whether there are currently any prospective customers about to sign on the dotted line to generate much needed revenue for our company, how many NDAs are currently in place and how many of those will eventually translate into a deal beyond initial evaluation.
None of us without insider knowledge can predict whether our company’s roadmap will go ahead as planned (which would be awesome), how our non-neuromorphic and neuromorphic competitors in the Edge AI space will develop over the next few years and how many downselections we will or we won’t make because of them, which employees will leave us and why, what exceptional new talents we will be able to attract, how global politics will play out and how world markets will react, what effect certain decisions taken by our management will have on the BRN share price, whether or not BrainChip will be acquired one day etc.
All these and other hard or impossible to predict factors may play a role to a larger or lesser extent. Excellent technology alone does not guarantee a “successful outcome” in a commercial sense.
Yes, the groundwork has been laid, the ecosystem is being built, and the validation by others is highly encouraging. But “successful” also means that more deals generating meaningful revenue need to follow at some point. And so we wait. Understandably, patience is wearing thin for many long-term holders, especially those whose age and/or health realistically means they cannot afford to wait for much longer, if they themselves - and not their children and grandchildren, if they even have any - would like to enjoy the juicy fruit they have been hoping to harvest.
Yet, some frustrated posters blame BRN management only and overlook the fact that it was also fellow forum users here and elsewhere that have been creating unrealistic expectations and hype over the years for which the company cannot be made solely responsible. My BrainChip journey has also been an intriguing psychology class about the world of echo chambers.
Maybe that’s not exactly the response you wanted to hear from me, but I’m afraid I can’t answer your question in a more honest way than that.
Now before you had initially asked me your question the other day, I had actually asked you to please provide proof to support your claim that there are Accenture patents that do not refer to Loihi as a “neuromorphic research chip” at some point in the patent.
So since it was you who brought up the topic of courtesy yourself, please now also have the courtesy to respond to me on that matter. I won’t frame it the same way as you did with me, though, by claiming you chose not to respond, but instead will give you the benefit of doubt that it may have merely slipped your mind.
Best regards
Frangipani
️ ️
Learning
Learning to the Top 🕵♂️
#mercedesbenz #electric #innovation | Markus Schäfer | 55 comments
Allow me to introduce the stunning new Vision Iconic show car. With this automotive masterpiece, Mercedes-Benz shows what the future can look like when it’s shaped by tradition, while remaining uncompromisingly modern. Let’s start with the extraordinary exterior. The silhouette appears to be...
Learning
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#mercedesbenz #electric #innovation | Markus Schäfer | 55 comments
Allow me to introduce the stunning new Vision Iconic show car. With this automotive masterpiece, Mercedes-Benz shows what the future can look like when it’s shaped by tradition, while remaining uncompromisingly modern. Let’s start with the extraordinary exterior. The silhouette appears to be...
Learning
Thanks for sharing @Learning.
Does that say Lidar System?
Neuromorphic Computing in 2025: How Brain-Inspired AI is Revolutionizing Technology https://www.linkedin.com/pulse/neur...w-brain-inspired-ai-technology-eshwar-k-dlyke
Frangipani
Top 20
Yup, BrainChip is indeed coming to Semiconductor Australia, although not participating in the above panel discussion “Leading from the Front” with DroneShield.
Instead, Sean Hehir will be representing BrainChip in Session Two “Semiconductors Power the Universe” alongside the CEOs of Weebit Nano (according to whom our two companies had been in discussions with “on multiple fronts” at the time of a March 2025 Retail Investor Briefing), 2D Generation and Archer Materials.
Agenda | SemiconductorAus
www.semiconductoraustralia.com
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#mercedesbenz #electric #innovation | Markus Schäfer | 55 comments
Allow me to introduce the stunning new Vision Iconic show car. With this automotive masterpiece, Mercedes-Benz shows what the future can look like when it’s shaped by tradition, while remaining uncompromisingly modern. Let’s start with the extraordinary exterior. The silhouette appears to be...
Learning
https://www.bing.com/images/search?...tomobile&cw=1006&ch=704&ajaxhist=0&ajaxserp=0
The original Jonckheere Aerodynamic Coupe was built by Belgium by Henri Jonckheere and his son Joseph Jonckheere in 1935. Starting from a 1925 Rolls-Royce Phantom I they added a streamlined coachwork with twin sunroofs.
Kingkong2015
Regular
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#mercedesbenz #electric #innovation | Markus Schäfer | 55 comments
Allow me to introduce the stunning new Vision Iconic show car. With this automotive masterpiece, Mercedes-Benz shows what the future can look like when it’s shaped by tradition, while remaining uncompromisingly modern. Let’s start with the extraordinary exterior. The silhouette appears to be...
Learning
Wow — BrainChip’s Akida is featured in the new Mercedes show car! I’m not entirely sure if the Level 4 automated driving system with LiDAR is powered by Akida, but it seems highly likely. Otherwise, BrainChip probably wouldn’t have suddenly published that LiDAR point cloud
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Taking a positive view there are reported to be an increasing number of Neuromorphic start ups which we are far more advanced than. They will 'head hunt' Brainchip staff for sure. It's an indication that the industry is growing.Yet another BRN employee has left…
Our former ML Solutions Architect Dhvani Kothari is now an AI Solutions Architect with Contextual AI:
#contextualai #genai #techcareer | Dhvani Kothari | 39 comments
🎉 Excited to share that I've joined Contextual AI as a Solutions Architect! 🚀 The innovation and vision here are inspiring, and I’m eager to contribute to what’s next in enterprise AI. I want to take a moment to express my heartfelt gratitude to everyone at BrainChip for an incredible...
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Kingkong2015
Regular
In Level 4 (L4) automated driving, a LiDAR (Light Detection and Ranging) system is one of the most critical sensors enabling the vehicle to perceive and understand its surroundings without human intervention. Here's what it does in detail:
LiDAR emits laser pulses (typically millions per second) and measures how long they take to bounce back from surrounding objects.
LiDAR helps the vehicle know exactly where it is on a map with centimeter-level accuracy.
L4 systems combine LiDAR data with inputs from:
With the detailed 3D model of its surroundings:
L4 autonomous vehicles rely on multiple LiDAR units (e.g., 360° roof-mounted plus corner LiDARs) to ensure complete coverage and fail-safe operation.
This redundancy is vital for hands-off, eyes-off driving — a key requirement for Level 4 autonomy.
1. 3D Environment Mapping
LiDAR emits laser pulses (typically millions per second) and measures how long they take to bounce back from surrounding objects.- This creates a high-resolution 3D “point cloud” — a map of the car’s environment in real time.
- It provides exact distance, size, and shape information of obstacles, road edges, buildings, pedestrians, and vehicles — far more accurate than a camera.
2. Object Detection and Tracking
- The system can identify and classify nearby objects (cars, bikes, humans, road barriers, etc.).
- It continuously tracks the position and speed of moving objects, helping predict their behavior (e.g., a pedestrian crossing the road).
3. Localization
LiDAR helps the vehicle know exactly where it is on a map with centimeter-level accuracy.- By comparing the current LiDAR point cloud to a high-definition (HD) map, the vehicle can determine its precise position and orientation, even where GPS is unreliable (like tunnels or urban canyons).
4. Sensor Fusion
L4 systems combine LiDAR data with inputs from:- Cameras (for color, traffic signs, lane markings)
- Radar (for speed and distance in poor weather)
- Ultrasonic sensors (for close-range detection)
5. Path Planning and Obstacle Avoidance
With the detailed 3D model of its surroundings:- The car’s AI can plan safe paths, calculate maneuvers, and avoid collisions in real time.
- In L4 autonomy, the LiDAR data supports redundant decision-making, meaning the vehicle can still drive safely even if other sensors fail.
6. Redundancy and Safety
L4 autonomous vehicles rely on multiple LiDAR units (e.g., 360° roof-mounted plus corner LiDARs) to ensure complete coverage and fail-safe operation.This redundancy is vital for hands-off, eyes-off driving — a key requirement for Level 4 autonomy.
Summary Table
| Function | Description | Why It Matters for L4 |
|---|---|---|
| 3D Mapping | Builds real-time environment model | Enables perception in all lighting conditions |
| Object Detection | Identifies vehicles, pedestrians, etc. | Critical for collision avoidance |
| Localization | Determines exact position | Keeps the vehicle correctly aligned with maps |
| Sensor Fusion | Combines LiDAR with radar/cameras | Increases reliability and accuracy |
| Path Planning | Guides safe navigation | Enables full autonomy without driver input |
| Redundancy | Backup sensing system | Ensures safety in system failures |
Kingkong2015
Regular
LiDAR + BrainChip Akida (L4 Mercedes) vs Tesla Full Vision Autonomous Driving
LiDAR + BrainChip Akida (L4 Mercedes)
Pros:
True 3D Perception: LiDAR provides centimeter-accurate distance and shape data in real time.- Superior Depth and Object Recognition: Does not rely on estimating depth from 2D images.
- All-Weather and Night Operation: Works reliably in low light, fog, rain, or glare.
- Hardware Redundancy: Combines LiDAR, radar, and cameras for safety-critical backup.
- Ultra-Low Latency Processing: Akida’s neuromorphic chip handles LiDAR point clouds at microsecond speed.
- Energy-Efficient: Consumes only a few hundred milliwatts — ideal for edge AI.
- On-Device Learning: Can adapt locally without needing cloud retraining.
- Regulatory Path to L4: Meets ISO 26262 (ASIL-D) safety requirements, enabling certified driverless modes.
- Scalable Edge Autonomy: Enables distributed intelligence across vehicle sensors.
Cons:
Higher Hardware Cost: LiDAR sensors and neuromorphic chips add expense.- Complex Integration: Requires sophisticated sensor-fusion and calibration.
- Bulkier Setup: Multiple LiDAR units increase weight and design complexity.
- Limited Data Scale: Fewer vehicles collecting real-world data compared with Tesla’s fleet.
Tesla Full Vision System (FSD)
Pros:
- Low Hardware Cost: Uses inexpensive cameras only.
- Sleek Design: No LiDAR domes — simple aesthetics.
- Fleet Learning Advantage: Millions of cars continuously send data for model improvement.
- Rapid Software Iteration: Updates can be rolled out over-the-air easily.
- Excellent Visual Pattern Recognition: Leverages massive datasets for visual reasoning.
Cons:
- 2D-Only Perception: Estimates depth; less reliable in 3D spatial mapping.
- Poor Performance in Bad Weather or Night: Camera vision degrades with fog, rain, or glare.
- High Compute Load: Requires large GPU processing power and high energy.
- No True Sensor Redundancy: Fails if cameras are blinded or obstructed.
- No On-Device Learning: Models must be retrained centrally.
- Regulatory Limitations: Currently approved only for L2–L3 assist, not L4 autonomy.
Overall Comparison
| Category | LiDAR + Akida (L4 Mercedes) | Tesla FSD (Vision-Only) | Which is More Advanced |
|---|---|---|---|
| Depth Perception | Direct 3D laser distance | 2D image estimation | LiDAR + Akida |
| Weather Reliability | Works in fog, rain, night | Vision fails in poor conditions | LiDAR + Akida |
| Processing Efficiency | Neuromorphic low-power chip | GPU-heavy, high energy | LiDAR + Akida |
| Learning Adaptation | On-device continual learning | Central server training only | LiDAR + Akida |
| Safety Redundancy | Multi-sensor failover | Vision only | LiDAR + Akida |
| Regulatory Readiness | Meets ASIL-D standards | L2/L3 only (FSD beta) | LiDAR + Akida |
| Cost and Simplicity | High cost / complex setup | Low cost / simple hardware | Tesla FSD |
| Fleet Data Scale | Limited deployment | Massive global fleet | Tesla FSD |
Verdict
LiDAR + BrainChip Akida (Mercedes L4) is more advanced in terms of:
- Real-time 3D perception
- Safety redundancy
- Regulatory compliance
- Energy efficiency
- Neuromorphic edge AI
Tesla Vision-Only FSD is more scalable and cost-efficient, ideal for rapid consumer deployment,
but not yet capable of certified Level 4 autonomy due to its limited sensor redundancy and environmental reliability.
Personal Opinion
In my view, the LiDAR + BrainChip Akida approach represents a major leap forward in autonomous driving.Its event-based neuromorphic processing allows it to handle LiDAR point clouds and sensor data much faster and far more efficiently than Tesla’s purely vision-based Full Self-Driving system.
While Tesla’s system relies on heavy GPU computation and large centralized training pipelines, Akida’s spiking neural network architecture processes events as they happen, using a fraction of the power and latency.
This results in real-time situational awareness, low energy consumption, and higher reliability, especially in complex or unpredictable environments where vision alone struggles.
In short, this new approach processes information more like a human brain than a camera, giving it a decisive edge in achieving true Level 4 autonomy.
Boab
I wish I could paint like Vincent
Everything old is new again. Although this is an Alfa RomeoView attachment 91961
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#mercedesbenz #electric #innovation | Markus Schäfer | 55 comments
Allow me to introduce the stunning new Vision Iconic show car. With this automotive masterpiece, Mercedes-Benz shows what the future can look like when it’s shaped by tradition, while remaining uncompromisingly modern. Let’s start with the extraordinary exterior. The silhouette appears to be...
Learning
Boab
I wish I could paint like Vincent
Edit, sorry Dodgy you beat me to it.
Learning
Learning to the Top 🕵♂️
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Considering Mercedes stated they used Akida technology a few years ago, Akida is the only commercially available technology that can do this type of compute (I think and happy to be corrected), and many many years ago now it was the ceo at the time (Louis DiNardo) who made a comment along the lines of 'we've found our sweet spot with LiDAR' all provide a fairly good argument for why Akida might be being used.
FJ-215
Regular
Important to remember that we are not alone!!!
Mercedes taps Intel Loihi2 for neuromorphic AI
Mercedes taps Intel Loihi2 for neuromorphic AI
Mercedes is leading a project in Germany to use neuromorphic computing to improve the performance of forward-facing automotive radar systems.
Mercedes is using the Intel Loihi 2 neuromorphic event-driven AI processor in the Naomi4radar project, says Markus Schäfer, Member of the Board of Management of Mercedes-Benz Group and Chief Technology Officer for Development & Procurement. Using neuromorphic, event driven algorithms and processors can increase the speed of response of the radar systems, he says.
Tothemoon24
Top 20
Today at EDHPC 2025, Evgenios Tsigkanos presented our approach to Satellite-as-a-Service, orchestrating ML workloads across various edge hardware platforms like a cloud cluster. 
Our demo highlighted applications such as vessel, cloud, flood, and fire detection. We harnessed the power of different hardware accelerators, including Xilinx Kria DPU, BrainChip Akida, Coral TPU, and Myriad 2, for representative on-board inference and performance. Our in-house AI models, along with compatibility for third-party models, enable on-board inference, reducing latency for quicker decisions, conserving bandwidth by transmitting processed data, and ensuring operation through communication gaps.
We extend our gratitude to our industry and academic collaborators, as well as ESA, whose efforts helped us navigate the hardware landscape.
Join us at our next presentation on Thursday at 14:40 in the Conference Room, where Evgenios Tsigkanos will present our work on "Synthetic Data Generators for Enhanced Space-based Network Traffic Modeling." We look forward to exchanging insights wit#edhpc2025 #edgeai #esa #onboardprocessing #aiinspace #kubernetes #mlops #satelliteasaservice | OHB Hellas
🌌 Today at EDHPC 2025, Evgenios Tsigkanos presented our approach to Satellite-as-a-Service, orchestrating ML workloads across various edge hardware platforms like a cloud cluster. 🚀 Our demo highlighted applications such as vessel, cloud, flood, and fire detection. We harnessed the power of...
Frangipani
Top 20
The revised GIASaaS (Global Instant Satellite as a Service, formerly Greek Infrastructure for Satellite as a Service) concept website by OHB Hellas is finally back online - and Akida now no longer shows up as UAV1 (see my post above) but as one of three SATs! 🛰
AKIDA, the envisioned SAT with the AKD1000 PCIe Card onboard, is slated to handle both Object Detection as well as Satellite Detection, while the planned KRIA and CORAL satellites (equipped with a Xilinx KRIA KV260 Vision AI Starter Kit resp. a Google Coral TPU Dev Board) are tasked to handle Vessel Detection, Cloud Detection and Fire Detection (for some reason OHB Hellas removed Flood Detection from the list of applications).
Please note that this is currently a concept website only.
The idea behind GIASaaS as well as mock 3D-printed versions of the yet-to-be-lauched satellites were presented at DEFEA 2025 - Defense Exhibition Athens earlier this month, as @Stable Genius had spotted on LinkedIn:
GIASaaS by OHB Hellas
giasaas.eu
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Evgenios Tsigkanos from OHB Hellas presented GIASaaS
at EDHPC 2025 today:#edhpc2025 #edgeai #esa #onboardprocessing #aiinspace #kubernetes #mlops #satelliteasaservice | OHB Hellas
🌌 Today at EDHPC 2025, Evgenios Tsigkanos presented our approach to Satellite-as-a-Service, orchestrating ML workloads across various edge hardware platforms like a cloud cluster. 🚀 Our demo highlighted applications such as vessel, cloud, flood, and fire detection. We harnessed the power of...
Gilles Bézard and Alf Kuchenbuch were also going to refer to GIASaaS in their two-part tutorial at EDHPC 2025 yesterday:
Here is more info on the two-part tutorial Gilles Bézard and Alf Kuchenbuch will be presenting on 13 October at EDHPC25 in Elche, Spain:
[All the weird extra os under “Use cases for Neuromorphic Processing in space” are evidently bullet points gone rogue in the website layout… When I copy and paste that passage from below website, the combination “space:” followed by “o” shows up as “space” here on TSE
]
Tutorials - 25M02 - EDHPC 2025 - Centro De Congresos De Elche
atpi.eventsair.com
Neuromorphic AI in Space Tutorials (by BrainChip)
Introduction
In space applications, every milliwatt matters. Satellites rely on ultra-low-power chips to process data on-board, where sending everything back to Earth is often impossible or inefficient. This makes efficient machine learning deployment on embedded hardware not just useful, but essential.
The goal of the tutorials is to show how advanced AI capabilities can be packed into tiny, power-constrained devices, enabling smarter, faster, and more autonomous satellite systems.
Audience
Project managers (particularly part 1) and engineers working on embedded low-power AI applications.
Level: from beginner to expert.
What will you learn?
In part 1: Akida in Space – Bringing Autonomy, Robustness and Efficient Data Transmission to Space Vehicles, you will learn:
- Why AI in Space?
- Why neuromorphic AI in Space?
- BrainChip Akida IP is the only Event-based AI on the commercial market – IP and silicon
- Use cases for Neuromorphic Processing in space
Lunar landingo Docking in spaceo Earth observationo Space Situational Awarenesso Satellite detection (use case OHB Giasaas)
In part 2: Bringing AI to the Edge – End-to-End Machine Learning Deployment on an Embedded Low-Power AI Neuromorphic HW, you will learn:
- Fetch and prepare a dataset suitable for your target application.
- Design and train a machine learning model using TF/Keras.
- Apply quantization techniques to reduce model size and optimize it for embedded hardware.
- Convert the trained model into a format compatible with Akida hardware toolchain.
- Export the model as a C source file that can be included and compiled together with your C main application.
- Integrate the Akida model binary into a baremetal C application running on a STM32 microcontroller.
- Run inference on Akida ultra-low-power hardware device, demonstrating efficient on-board processing for space-constrained environments such as satellites.
Outline of hands-on tutorial
In the first tutorial, we teach why there is a paradigm shift in Space from purely deterministic classic programming to the use of low power AI in specific use cases. We show the vast improvement in capabilities coming with the use of AI in Space.
In the second tutorial, we demonstrate how to take an ML model from dataset preparation all the way to baremetal deployment on a microcontroller with a hardware accelerator. By the end, you will know how to take an ML project from concept to fully optimized embedded deployment, step by step.
Speakers:
- Gilles Bézard (BrainChip) – Part 1 & 2
- Alf Kuchenbuch (BrainChip) – Part 1
As you can see, Gilles Bézard and Alf Kuchenbuch will also be referring to the GIASAAS (Global Instant Satellite as a Service) concept by OHB Hellas, which I first posted about yesterday a year ago…
https://thestockexchange.com.au/threads/brn-discussion-ongoing.1/post-438109
… and gave an update on in May:
To the best of my knowledge, this is the first time BrainChip will be making a reference to GIASAAS.
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