BRN Discussion Ongoing

[Tothemoon24]

Traditional LiDAR systems depend on the cloud, creating delays that limit how fast machines can respond. Akida PointNet++ brings AI processing directly to the edge, reducing latency by up to 80% and operating on just a few milliwatts of power.

This means faster perception, safer navigation, and smarter automation for vehicles, drones, robots, and intelligent infrastructure.

With Akida’s efficient on-chip design, devices can interpret complex 3D environments instantly without relying on cloud connectivity.

Learn how BrainChip’s Akida PointNet++ is shaping the future of spatial AI.
https://lnkd.in/gzbyf9Kv

LiDAR Point Cloud_LP

Unleash Real-Time LiDAR Intelligence With On-Chip Point Cloud AI Accelerating LiDAR Point Cloud with BrainChip’s AkidaTM PointNet++ Model. Learn More About LiDAR Intelligence The Essential Role of LiDAR Point Cloud AI For next-generation robotics, automotive systems, and...

brainchip.com

 

[7für7]

Traditional LiDAR systems depend on the cloud, creating delays that limit how fast machines can respond. Akida PointNet++ brings AI processing directly to the edge, reducing latency by up to 80% and operating on just a few milliwatts of power.

This means faster perception, safer navigation, and smarter automation for vehicles, drones, robots, and intelligent infrastructure.

With Akida’s efficient on-chip design, devices can interpret complex 3D environments instantly without relying on cloud connectivity.

Learn how BrainChip’s Akida PointNet++ is shaping the future of spatial AI.
https://lnkd.in/gzbyf9Kv
View attachment 91807

LiDAR Point Cloud_LP

Unleash Real-Time LiDAR Intelligence With On-Chip Point Cloud AI Accelerating LiDAR Point Cloud with BrainChip’s AkidaTM PointNet++ Model. Learn More About LiDAR Intelligence The Essential Role of LiDAR Point Cloud AI For next-generation robotics, automotive systems, and...

brainchip.com

Thanks for the post! I also was about to post the same and deleted it…..

I just have one question… When I post something, I usually write one or two sentences about what it’s about and then add the link.

I’ve noticed that you often also take a screenshot of the post’s header with the BrainChip logo, copy and paste the text separately, and then also include the link — all good, of course — but why the screenshot of the header with the BrainChip logo?

 

[7für7]

Sorry if already posted

“BrainChip experts host an interactive webinar showcasing the benefits of executing AI/ML inference models on BrainChip’s Akida 2 remotely using Akida Cloud.”

Akida Cloud Webinar

BrainChip experts host an interactive webinar showcasing the benefits of executing AI/ML inference models on BrainChip’s Akida 2 remotely using Akida Cloud. Akida Cloud lets you execute models, assess accuracy, and benchmark Akida hardware performance all from your desktop. Easily evaluate...

brainchip.com

 

[Taproot]

Home

Parsons is a digitally enabled solutions provider and a leader in many diversified markets with a focus on national security, defense, and global infrastructure.

www.parsons.com

 

[7für7]

It’s a long time ago since we talked about gaps… is……is there any gap to….is there any gap to close?

 

[Mccabe84]

Fingers crossed we get a short squeeze as they might not be the ones buying

 

[equanimous]

in response to the speeding ticket yesterday Brainchip has decided to go again....

 

[7für7]

Here is the LinkedIn post regarding Akida…LiDAR

#lidar #edgeai #spatialintelligence #artificialintelligence #3dperception #autonomousvehicles #robotics #semiconductors #machinelearning #akida #brainchip #aiinnovation #edgecomputing… | BrainChip

Traditional LiDAR systems depend on the cloud, creating delays that limit how fast machines can respond. Akida PointNet++ brings AI processing directly to the edge, reducing latency by up to 80% and operating on just a few milliwatts of power. This means faster perception, safer navigation, and...

www.linkedin.com

 

[Bravo]

The partnership between Arduino and BrainChip was announced in October 2022

Just to clarify, BrainChip and Arduino have not formally announced a partnership to date. However there is a connection between Brainchip and Arduino via the Edge Impulse ecosystem.

• In May 2022, BrainChip and Edge Impulse officially announced a partnership to accelerate AI/ML deployments on BrainChip’s Akida platform.
• In October 2022, Arduino and Edge Impulse announced a white-label partnership, integrating Edge Impulse Studio directly into Arduino’s online tools.

In a post dated 21 January 2024 (see below), Fact Finder highlighted a project by Christopher Mendez (PRO Content Creator at Arduino) that used BrainChip’s AKD1000 with Edge Impulse to control a TV, air conditioner, or light simply by pointing at them. Using the Akida Development Kit and a Logitech BRIO 4K Webcam, the model achieved excellent accuracy and ultra-low power consumption. Mendez concluded:

"This project leverages the Brainchip Akida Neuromorphic Hardware Accelerator to propose an innovative solution to home automation. It can be optimized to work as a daily used gadget that may be at everyone's house in the near future.”

Fast-forward to today and Qualcomm has now acquired both Arduino (this week) and Edge Impulse (earlier this year), bringing them under the same umbrella.

I believe this creates a very interesting pathway where BrainChip could possibly integrate through Edge Impulse’s existing Akida support into the Arduino ecosystem via Qualcomm’s new edge AI stack. Or Qualcomm may encourage developer cross-pollination among its new assets (Arduino + Edge Impulse + Snapdragon Spaces, etc).

So while there’s still no direct BrainChip / Arduino partnership, the ecosystem connections have become much closer and potentially far more strategic.

At least that's what I'm hoping.




Link here #74,284

 

[7für7]

in response to the speeding ticket yesterday Brainchip has decided to go again....

View attachment 91809

Let’s see where we end up on Friday… latest Monday… I think the shorter will take every cent no matter what…. I don’t trust that sh…

 

[Pom down under]

2025 A.I. Awards Finalists | Cloud Computing & SaaS Awards

www.cloud-awards.com

 

[Bravo]

Traditional LiDAR systems depend on the cloud, creating delays that limit how fast machines can respond. Akida PointNet++ brings AI processing directly to the edge, reducing latency by up to 80% and operating on just a few milliwatts of power.

This means faster perception, safer navigation, and smarter automation for vehicles, drones, robots, and intelligent infrastructure.

With Akida’s efficient on-chip design, devices can interpret complex 3D environments instantly without relying on cloud connectivity.

Learn how BrainChip’s Akida PointNet++ is shaping the future of spatial AI.
https://lnkd.in/gzbyf9Kv
View attachment 91807

LiDAR Point Cloud_LP

Unleash Real-Time LiDAR Intelligence With On-Chip Point Cloud AI Accelerating LiDAR Point Cloud with BrainChip’s AkidaTM PointNet++ Model. Learn More About LiDAR Intelligence The Essential Role of LiDAR Point Cloud AI For next-generation robotics, automotive systems, and...

brainchip.com

Quite the coincidence if you ask me.

I was just thinking that BrainChip’s new Akida PointNet++ may be the missing piece in that puzzle behind Qualcomm’s acquisition of Arduino, to accelerate its push into robotics and intelligent automation, as discussed a little earlier.

I don't believe that Qualcomm currently has any true equivalent to Akida PointNet++, especially when it comes to real-time, neuromorphic 3D point-cloud processing at the edge, which is a critical capability for next-generation robotics, drones, and autonomous systems.

By processing LiDAR and 3D spatial data directly at the edge, Akida PointNet++ delivers sub-10 ms spatial awareness while drawing only milliwatts of power, potentially giving Qualcomm exactly what its robotics roadmap needs - faster perception, longer battery life, and smarter autonomy.

Combined with Arduino + Edge Impulse (Qualcomm), this could finally democratize neuromorphic robotics, with Akida quietly powering the spatial intelligence beneath it all.

Here's hoping!

 

[smoothsailing18]

Traditional LiDAR systems depend on the cloud, creating delays that limit how fast machines can respond. Akida PointNet++ brings AI processing directly to the edge, reducing latency by up to 80% and operating on just a few milliwatts of power.

This means faster perception, safer navigation, and smarter automation for vehicles, drones, robots, and intelligent infrastructure.

With Akida’s efficient on-chip design, devices can interpret complex 3D environments instantly without relying on cloud connectivity.

Learn how BrainChip’s Akida PointNet++ is shaping the future of spatial AI.
https://lnkd.in/gzbyf9Kv
View attachment 91807

LiDAR Point Cloud_LP

Unleash Real-Time LiDAR Intelligence With On-Chip Point Cloud AI Accelerating LiDAR Point Cloud with BrainChip’s AkidaTM PointNet++ Model. Learn More About LiDAR Intelligence The Essential Role of LiDAR Point Cloud AI For next-generation robotics, automotive systems, and...

brainchip.com

AI Overview

+1

Over 1.6 million LiDAR units were shipped globally in 2024, a figure that is expected to quadruple by 2030, driven primarily by increasing adoption in passenger cars. The market's massive growth is a clear indicator of the increasing demand for LiDAR technology across various sectors, including autonomous vehicles and advanced driver-assistance systems (ADAS).

 

[Bravo]

Nice to see the ol' share price continuing to head in the right direction!

Hot diggity dog!

 

[Pom down under]

Nice to see the ol' share price continuing to head in the right direction!

Hot diggity dog!



View attachment 91816

 

[miaeffect]

Nice to see the ol' share price continuing to head in the right direction!

Hot diggity dog!



View attachment 91816

Your tub time is coming

 

[smoothsailing18]

From the other site

Here are some benchmarks comparing PointNet++ running on Akida 2 vs NVIDIA Jetson Xavier NX and Orin NX for real-time LiDAR classification at the edge.

1. Benchmark Sources​

• Akida PointNet++: BrainChip LiDAR Point Cloud Model brochure (Oct 2025).
• Jetson Xavier NX / Orin NX: Derived from public PyTorch PointNet++ benchmarks on ModelNet40 and KITTI, using TensorRT-optimized inference (batch = 1).
  • Xavier NX: 384 CUDA cores, 21 TOPS INT8
  • Orin NX: 1024 CUDA cores, 100 TOPS INT8
• Power figures are measured in NVIDIA’s MaxN mode, which reflects real deployment on drones / robotics platforms.

2. PointNet++ Performance Comparison​

Metric	Akida 2	Jetson Xavier NX	Jetson Orin NX
FPS (ModelNet40)	183 FPS	65 – 85 FPS (FP16/INT8)	110 – 135 FPS (FP16/INT8)
Latency / Frame	5 – 6 ms	12 – 15 ms	7 – 9 ms
Power	50 mW	10 – 15 W	15 – 25 W
Energy / Inference	0.28 mJ	~150 – 200 mJ	~200 – 300 mJ
Model Size	1.07 MB	~10 – 12 MB	~10 – 12 MB
Accuracy (ModelNet40)	81.6 % (4-bit QAT)	89 – 90 % (FP32 baseline)	89 – 90 % (FP32 baseline)
Deployment Mode	Always-on, ultra-low power	Embedded GPU (fan/heat dissipation required)	High-end embedded GPU

3. What This Shows​

1. Throughput
   • Akida 2 actually outperforms Xavier and Orin NX on raw FPS, despite being neuromorphic and consuming three orders of magnitude less power.
   • Orin NX gets closer but still lags slightly at similar batch sizes.
2. Power & Energy
   • Akida’s ~50 mW is in a completely different regime than the 10–25 W Jetson modules.
   • That’s ~500×–1000× lower energy per inference, which is decisive for always-on payloads (e.g. drones, satellites, smart infrastructure).
3. Accuracy Trade-off
   • Akida’s quantized model (4-bit QAT) loses ~8 % absolute accuracy vs FP32, but this is expected and often acceptable for edge classification, especially if upstream sensor fusion provides redundancy.
4. Form Factor / Thermal
   • Jetsons need active cooling and steady power supply — not trivial on space or micro-UAV platforms.
   • Akida can operate fanless, battery-powered, or solar-powered.

4. Strategic Takeaway​

Use Case	Best Fit
Battery-powered always-on LiDAR classification (e.g., satellite autonomy, drones, infrastructure nodes)	Akida 2 — ultra-low power, high FPS, compact
Onboard AI co-processor with larger perception stack (e.g., autonomous cars, ground robots)	Jetson Orin NX — higher model flexibility, better FP32 accuracy, but power-hungry
Mixed sensor fusion payloads with strict SWaP (e.g., ESA cubesats, tactical drones)	Akida as front-end classifier + Jetson/FPGA for downstream fusion or planning

Summary Table​

Feature	Akida 2	Jetson Xavier NX	Jetson Orin NX
FPS	183	65–85	110–135
Power	0.05 W	10–15 W	15–25 W
Energy/Inference	0.28 mJ	150–200 mJ	200–300 mJ
Accuracy	81.6 %	≈ 90 %	≈ 90 %
Edge Suitability	Always-on	Thermally constrained	High-end only

Bottom line:​

For PointNet++ at the edge, Akida 2 outperforms Jetson Xavier NX and Orin NX on raw FPS, power, and energy efficiency, with a modest accuracy gap from quantization that can be narrowed through improved training and model updates. This is why BrainChip is targeting Akida PointNet++ for autonomous drones, satellites, and infrastructure nodes — it's built for tiny, always-on LiDAR intelligence rather than general AI workloads.

*GPT5

 

[Pom down under]

Your tub time is coming
View attachment 91818

 

[Tothemoon24]

Stocks are rising & so am I

The Hidden Tech in Your Next Wearable: Why Spiking is the New Speed and Efficiency in Hardware.

Most of the AI buzz focuses on massive models in cloud data centers. But the real revolution in low-power intelligence is happening at the very edge: through Neuromorphic Computing.

The challenge for devices like advanced health trackers, industrial sensors, or autonomous drone navigation is simple: continuous processing with crippling power constraints. Running traditional Deep Learning on these devices drains the battery almost instantly.

The solution is brilliant—and brain-inspired.

Enter the Spiking Neural Network (SNN):

Unlike a conventional chip that processes data in large, power-hungry blocks, neuromorphic chips use Spiking Neural Networks (SNNs). These networks only fire (process) a signal when the input data exceeds a certain threshold—just like a biological neuron. This is known as event-based processing.

Why this is a game-changer for niche industries:

1. Extreme Efficiency: A 100x to 1000x improvement in energy efficiency compared to standard GPUs for certain tasks. Imagine a sensor that lasts for years, not days.
2. Instant Reaction: Processing occurs in real-time, right where the data is created (Edge AI), making it perfect for safety-critical systems like autonomous vehicles or real-time medical monitoring where latency can't be tolerated.
3. Adaptive Learning: The SNN architecture inherently supports "one-shot" or continuous on-device learning, adapting to its environment without massive retraining cycles.

If you are a Product Manager, Hardware Engineer, or Investor building solutions that require high-speed, continuous processing under severe power budgets, you need to be integrating this ecosystem now.

What niche application (outside of health) is perfectly primed for a move to neuromorphic processing? Share your 'killer app' idea below!

#NeuromorphicComputing #EdgeAI #HardwareInnovation #IoT #SNN #FutureofTech #DeepLearning

#neuromorphiccomputing #edgeai #hardwareinnovation #iot #snn #futureoftech #deeplearning | Noushad Yashan ( 牙山 / 야산 / ياشان )

The Hidden Tech in Your Next Wearable: Why Spiking is the New Speed and Efficiency in Hardware. Most of the AI buzz focuses on massive models in cloud data centers. But the real revolution in low-power intelligence is happening at the very edge: through Neuromorphic Computing. The challenge...

www.linkedin.com

 

[7für7]

@Esq.111 is your parrot Stil alive?