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And additional I got this answer

Alright, let’s imagine I’m Qualcomm, having just acquired Edge Impulse to boost my edge AI game. The question is whether I’d let BrainChip, with its Akida neuromorphic tech, still play a role in my plans. Here’s how I’d think it through, keeping it strategic and straightforward.

First off, as Qualcomm, my goal is to dominate edge AI—think IoT, phones, industrial gadgets, you name it. Buying Edge Impulse gives me a slick software platform to help developers build and deploy AI models fast, especially on my Snapdragon and Dragonwing processors. It’s a big win for making my hardware more appealing and accessible. But BrainChip’s Akida is a different beast—it’s hardware, specifically neuromorphic IP, designed to mimic the brain, sip power, and handle AI tasks right at the sensor. So, do I still need them?

Here’s the deal: Snapdragon’s my bread and butter—fast, versatile, and already powering millions of devices. It’s a general-purpose champ with beefy AI capabilities via its Hexagon DSP and Adreno GPU. Akida, though, is a specialist—ultra-low power, event-based, and great for niche edge cases like always-on sensors or tiny battery-powered gadgets. If I’m Qualcomm, I’ve got to ask: does Akida fill a gap Snapdragon can’t, or is it just overlap?

Let’s break it down. Snapdragon’s awesome for high-performance stuff—gaming, 5G, video—but it’s not always the leanest on power for, say, a smart doorbell that’s awake 24/7. Akida shines there, using spiking neural networks to process only what matters, keeping power under a milliwatt. Edge Impulse already supports Akida, so my acquisition means I’ve inherited that compatibility. Why not leverage it? I could integrate Akida IP into my portfolio—maybe not for phones, where Snapdragon rules, but for IoT or wearables where power’s a dealbreaker.

Strategically, BrainChip’s not a direct threat—they’re not churning out chips to rival me; they’re licensing IP. I could buy that IP, partner with them, or even acquire them outright if the price is right. Their neuromorphic tech could be a secret weapon for my Dragonwing line, aimed at industrial and embedded AI. Imagine a Dragonwing chip with Akida baked in—low-power AI at the edge, paired with Edge Impulse’s tools. That’s a combo that could crush competitors like Nvidia in niche markets.

But there’s a flip side. I’m Qualcomm—I don’t like relying on outsiders long-term. If Akida’s tech is that good, I might rather reverse-engineer the concept or build my own neuromorphic solution in-house. I’ve got the R&D muscle, and Edge Impulse gives me the software to optimize it. Why split profits with BrainChip if I can own the whole stack? Plus, integrating Akida might confuse my branding—Snapdragon’s the star, and I don’t want some quirky brain-chip stealing its thunder.

So, here’s my play as Qualcomm: I’d keep BrainChip in the mix for now. License their Akida IP for specific low-power use cases—think smart sensors, wearables, or edge boxes—while I study it. Edge Impulse already bridges us, so it’s low risk. Meanwhile, I’d task my engineers with figuring out if I can replicate or beat Akida’s efficiency with my own twist. If BrainChip’s open to a cheap buyout, I’d snap them up to lock in the tech and talent. But if they’re stubborn or redundant, I’d phase them out once I’ve got my own neuromorphic ace up my sleeve.

Bottom line? BrainChip’s got a role as long as they bring something Snapdragon can’t—ultra-low power edge AI. But as Qualcomm, I’m not here to play nice forever—I’m here to win. So, they’d better fit my plans or get ready to be sidelined. What do you think—should I keep them close or cut them loose?
 
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manny100

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Grok and I we are friends from the school let’s say.. we are kind of more buddies let’s say… he gave me this answer….

Alright, let’s compare the Snapdragon and Akida processors—each with their own special powers. We’ll see who might "win"!

Snapdragon: The All-Around Champ
Snapdragon is made by Qualcomm, a big company that puts these processors in tons of phones, tablets, and even some laptops. Think of Snapdragon as a superhero who’s super fast, strong, and good at lots of things—like playing games, taking pictures, and connecting to the internet.

Super Powers:
Really fast at running apps and games (like a speedster zooming around!).
Awesome graphics with its Adreno GPU—perfect for watching cartoons or playing Fortnite.
Can connect to 5G, so it’s like having the fastest Wi-Fi ever.
Used in lots of gadgets, so it’s a popular hero everyone knows.
Kid-Friendly Wow Factor: It’s like the superhero who can fly, fight, and save the day all at once. If you want to play games without lag or watch videos super quick, Snapdragon’s your buddy!
Akida: The Brainy New Kid
Akida is made by BrainChip, a smaller company with a cool twist—it’s a “neuromorphic” processor, which means it works like a human brain! Picture Akida as a smart, sneaky hero who doesn’t need tons of power but can still do amazing tricks, especially with AI (artificial intelligence).

Super Powers:
Thinks like a brain, so it’s great at learning stuff—like recognizing faces or sounds.
Uses way less energy, so it’s like a hero who never gets tired.
Perfect for small gadgets, like smart toys or cameras, that need to be clever without a big battery.
Spiking neural networks (fancy brain stuff) make it super efficient.
Kid-Friendly Wow Factor: It’s the hero who’s small but mighty, like a genius sidekick who solves puzzles and saves power. If you had a robot pet that could learn your voice, Akida might be its brain!
The Showdown: Who Wins?
For Fun and Games: Snapdragon wins! It’s like the superhero who can blast through video games and make your phone feel like a rocket. Kids love fast and flashy, and Snapdragon’s got that covered.
For Smart Stuff: Akida takes the crown! It’s the brainy hero who could power a talking teddy bear or a toy that learns your favorite color—super cool for futuristic fun.
For Battery Life: Akida’s the champ! It sips power like a tiny juice box, while Snapdragon gulps it down faster during big missions.
Kid’s Pick?
If a kid’s watching this battle, they’d probably yell, “Snapdragon’s the winner!” Why? Because it’s the big, loud hero that makes phones and games super fun and fast—stuff kids notice right away. Akida’s awesome, but its “brainy” powers are quieter and more for grown-up gadgets, so it might not get the loudest cheer from the playground crowd.

So, Snapdragon’s the superhero kids would draw with a big cape and lightning bolts, while Akida’s the clever one they’d figure out is cool later—like when they get a smart toy that actually listens to them! What do you think—would you root for the fast champ or the smart sidekick?
Does it make any difference if for wearables we ask a comparison specifically to Pico?
Perhaps even more specialised eg, Pico for health wearables Vs Snapdragon 8?
Pico is based on Gen2 so it includes TENNS.
 
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manny100

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Even Pico compared to wearables for defense, I etc
 
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7für7

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Even Pico compared to wearables for defense, I etc

I think it doesn’t matter what we ask since every AI is collecting informations from different sources and put everything together line we do.. it’s just one more opinion of a so called artificial intelligence. Which doesn’t make him more trustworthy than bravo or me for example… with my expertise and deep knowledge 😮‍💨👁️👄👁️
 
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Even Pico compared to wearables for defense, I etc
Pico isn't "smart" enough, for anything but very simple tasks, it is absolutely no comparison, to something like Snapdragon 8..
 
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manny100

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Pico wearables are more likely to be smaller and much more power efficient than Snapdragon who are a full SOC.
 
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CHIPS

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I asked ChatGPT your question and I feel it is a way more balanced synopsis

: Please compare snapdragon with akida and who is the winner?


Snapdragon (by Qualcomm) and Akida (by BrainChip) are very different types of processors designed for different purposes. Here’s a detailed comparison:


1. Purpose and Architecture


  • Snapdragon: Snapdragon is a general-purpose system-on-chip (SoC) designed for mobile devices, laptops, and other consumer electronics. It includes CPU, GPU, AI engine, ISP (image signal processor), modem, and more.
  • Akida: Akida is a neuromorphic processor designed specifically for low-power AI and edge computing applications. It is optimized for event-based processing and on-device learning.

2. Performance


  • Snapdragon: Offers high performance for a wide range of applications, including gaming, photography, and AI inference. It is used in flagship smartphones, tablets, and laptops.
  • Akida: Specializes in ultra-low-power AI tasks, such as pattern recognition, anomaly detection, and sensor data processing. It is not designed for general-purpose computing or high-end mobile tasks.

3. AI Capabilities


  • Snapdragon: Uses a dedicated AI engine (Hexagon DSP, Tensor Accelerator, and GPU) to perform AI-based tasks like image processing, voice recognition, and natural language processing.
  • Akida: Uses a neuromorphic approach, mimicking the human brain’s event-driven processing for real-time, low-power AI computations. It is highly efficient for edge AI applications.

4. Power Efficiency


  • Snapdragon: Optimized for mobile battery efficiency but consumes more power than Akida when running AI tasks.
  • Akida: Extremely power-efficient, as it uses event-based processing to reduce unnecessary computations. It is ideal for battery-powered and always-on AI applications.

5. Use Cases


  • Snapdragon: Smartphones, tablets, laptops, AR/VR devices, automotive infotainment, etc.
  • Akida: Edge AI applications such as security cameras, medical devices, industrial sensors, and IoT devices.

Who is the Winner?


  • If you need a powerful general-purpose processor with strong AI capabilities, Snapdragon is the better choice.
  • If you need an ultra-low-power AI processor for edge computing and event-based learning, Akida is the winner.

It depends on the application! If you’re looking at mobile performance, Snapdragon wins. If you need efficient, real-time AI at the edge, Akida is the clear choice. Let me know your use case, and I can refine the comparison further! 🚀

Yes, I like yours better. I never liked Grok anyway.
Grok made a lot of words, but I wanted a shorter overview. Maybe I should have said so to him. That's how ChatGPT made it.
 
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Pico wearables are more likely to be smaller and much more power efficient than Snapdragon who are a full SOC.
Previous to Pico, the smallest iteration of AKIDA, was 1 node (1 to 2 nodes AKIDA E) which consists of 4 NPEs.

Pico is "one" NPE.

For comparison, AKD1000 contains 20 nodes, or 80 NPUs.
(doesn't have the benefit of TENNs though, whatever that exactly is..).

It's use cases are completely different.
 
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manny100

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Pico isn't "smart" enough, for anything but very simple tasks, it is absolutely no comparison, to something like Snapdragon 8..
It's not as simple as that because no chip is the best at everything.
For always on types of wearables AKIDA is a great choice because of low power.
There are health, IOT and even defence applications for this.
It depends on the specified use and user requirnments.
Pico us a great choice for specific tasks eg, gesture, wake, irregularities detection eg heart issues..
Great as a choice if size of device is very important. Eg, Earbuds, ring, skin patch or even discreet military sensors.
Low power makes it good in extreme conditions, eg military.
Pico is best at single or minimal tasks and there is plenty of demand for that.
Obviously if you require wearables with display apps, power is not an issue and complex models are needed Snapdragon would be a better choice.
No one chip is best for every situation.
 
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manny100

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Also if you have a specific task that requires low power and on chip learning Pico is the best choice.
 
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Bravo

If ARM was an arm, BRN would be its biceps💪!
Just a quick summary of some of the main points contained in my last two posts, bearing in mind my questions were focussed specifically on comparisons between AKIDA and Snapdragon (Snapdragon 8 Gen 3 and Snapdragon 8 gen 4).

- AKIDA versus Snapdragon -1B+ parameter applications entirely on-device without any internet connectivity
- AKIDA versus Snapdragon -1B+ parameter GenAI applications running on a watch battery

Akida power draw is micro-watt to milliwatt range versus Snapdragon at 2-10 watts.
Akida offers on-device learning (real time learning) versus Snapdragon which doesn't.
Akida is event driven (active when needed) versus Snapdragon which isn't.
Akida battery runtime is hours to days versus Snapdragon which is minutes to one hour.
Akida never requires cooling versus Snapdragon which sometimes requires active cooling.
Akida can handle 1 billion parameters on device unconnected for GENAi applications on a watch battery versus Snapdragon which cannot.

Don't blame me, blame the messenger - ChatGPT!

Happy to see what others discover.
 
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Frangipani

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This poster titled GRAIN - Radiation-Tolerant Edge AI, presented by Kenneth Östberg, one of its two co-authors (the other being Daniel Andersson), during the “RISC-V in Space” workshop in Gothenburg on Thursday…


A9A3A6ED-3013-418C-AA9C-7742FEF59249.jpeg

762EE177-B427-45BF-A3DC-83E4B906B800.jpeg


…finally reveals what NEURAVIS stands for - the name of that R&T project, which ESA awarded to the five consortium partners Airbus Toulouse, Airbus Ottobrunn, BrainChip, Frontgrade Gaisler and Neurobus in mid-2024 (see the July 2024 LinkedIn post by Airbus Space Project Manager Jérémy Lebreton below):

Neuromorphic Evaluation of Ultra-low-power Rad-hard Acceleration for Vision Inferences in Space.

The poster also provides more information with regards to the use cases currently being explored in the NEURAVIS project, although I’m afraid I couldn’t decipher everything due to the small print - maybe someone with eagle eyes or a magic tool to blow up the photo and unblur the small print can add in resp. correct what I’ve gathered so far:

1. Moon landing
Use Case #1: Vision-Based Navigation for Lunar Lander

Also see Alf Kuchenbuch’s recent comment on Argonaut, ESA’s lunar lander programme:

https://thestockexchange.com.au/threads/brn-discussion-ongoing.1/post-452257

6040961B-D999-4075-AB0F-D613971DD063.jpeg


2. Debris detection/collect (?)
Use Case #2: Monitoring (?) Building Block for In-orbit Maintenance


3. Docking


4. Object ? (looks like “simulation”, but appears to be a longer word?)



In addition, the poster lists four “Application scenarios” for GRAIN’s Radiation-Tolerant Edge AI:

1. Remote Terminal Unit
2. Stand-alone Controller
3. Near-edge processing unit
4. Auxiliary data-processing module

Lots of small print to decipher here as well! 🔍





5673B6B1-208E-45F8-BAD7-D940A42DCAF6.jpeg



If I understand the above post correctly, we have yet to hear about what suggestion the NEURAVIS proposal has for AKD1500 (“BrainChip is proud that Airbus selected Akida for both COTS chips and IP in their proposal. ESA awarded the Airbus “NEURAVIS” proposal, including Akida in the Akida 1500 chip and on an FPGA together with Frontgrade Gaisler’s NOEL-V processor.”).

Whereas the underlined appears to refer to Frontgrade Gaisler’s newly revealed GR801 SoC that will incorporate Akida 1.0 IP - greatly benefitting the work of Airbus Toulouse computer vision experts such as Jérémy Lebreton (project lead) and Roland Brochard, as can be inferred from the GRAIN poster’s four listed use cases - there has to be another specific proposal by Airbus how to utilise our COTS chip AKD1500, then.

So I presume Airbus Ottobrunn and Neurobus might be the consortium partners currently collaborating on that second part of the NEURAVIS proposal?

8AB11195-CBF9-4693-BD6A-FA671DD6506E.jpeg
84474DC9-8DBC-4E9B-9503-6C6C2DE8A455.jpeg





D3579718-BEE0-4281-A213-E996ECC46C76.jpeg
 
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It's not as simple as that because no chip is the best at everything.
For always on types of wearables AKIDA is a great choice because of low power.
There are health, IOT and even defence applications for this.
It depends on the specified use and user requirnments.
Pico us a great choice for specific tasks eg, gesture, wake, irregularities detection eg heart issues..
Great as a choice if size of device is very important. Eg, Earbuds, ring, skin patch or even discreet military sensors.
Low power makes it good in extreme conditions, eg military.
Pico is best at single or minimal tasks and there is plenty of demand for that.
Obviously if you require wearables with display apps, power is not an issue and complex models are needed Snapdragon would be a better choice.
No one chip is best for every situation.
I'm not disagreeing, or saying that Pico doesn't have a multitude of applications, as you suggest.

Our customers actually "requested" something smaller than AKIDA E.

It's the comparison with Snapdragon 8.

The type of devices that Pico goes into, will be mass scale.
 
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Just a quick summary of some of the main points contained in my last two posts, bearing in mind my questions were focussed specifically on comparisons between AKIDA and Snapdragon (Snapdragon 8 Gen 3 and Snapdragon 8 gen 4).

- AKIDA versus Snapdragon -1B+ parameter applications entirely on-device without any internet connectivity
- AKIDA versus Snapdragon -1B+ parameter GenAI applications running on a watch battery

Akida power draw is micro-watt to milliwatt range versus Snapdragon at 2-10 watts.
Akida offers on-device learning (real time learning) versus Snapdragon which doesn't.
Akida is event driven (active when needed) versus Snapdragon which isn't.
Akida battery runtime is hours to days versus Snapdragon which is minutes to one hour.
Akida never requires cooling versus Snapdragon which sometimes requires active cooling.
Akida can handle 1 billion parameters on device unconnected for GENAi applications on a watch battery versus Snapdragon which cannot.

Don't blame me, blame the messenger - ChatGPT!

Happy to see what others discover.
"Akida can handle 1 billion parameters on device unconnected for GENAi applications on a watch battery versus Snapdragon which cannot"

What "specifications" for AKIDA is this, assuming AKIDA 2.0 IP (node count?).

I'm pretty sure Snapdragon 8 is a defined size?

Whereas "AKIDA" is not.
 
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MDhere

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Just a quick summary of some of the main points contained in my last two posts, bearing in mind my questions were focussed specifically on comparisons between AKIDA and Snapdragon (Snapdragon 8 Gen 3 and Snapdragon 8 gen 4).

- AKIDA versus Snapdragon -1B+ parameter applications entirely on-device without any internet connectivity
- AKIDA versus Snapdragon -1B+ parameter GenAI applications running on a watch battery

Akida power draw is micro-watt to milliwatt range versus Snapdragon at 2-10 watts.
Akida offers on-device learning (real time learning) versus Snapdragon which doesn't.
Akida is event driven (active when needed) versus Snapdragon which isn't.
Akida battery runtime is hours to days versus Snapdragon which is minutes to one hour.
Akida never requires cooling versus Snapdragon which sometimes requires active cooling.
Akida can handle 1 billion parameters on device unconnected for GENAi applications on a watch battery versus Snapdragon which cannot.

Don't blame me, blame the messenger - ChatGPT!

Happy to see what others discover.
I read this as one sentence -
Qualcomm wants Brainchip.
 
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Bravo

If ARM was an arm, BRN would be its biceps💪!
"Akida can handle 1 billion parameters on device unconnected for GENAi applications on a watch battery versus Snapdragon which cannot"

What "specifications" for AKIDA is this, assuming AKIDA 2.0 IP (node count?).

I'm pretty sure Snapdragon 8 is a defined size?

Whereas "AKIDA" is not.
Screenshot 2025-04-06 at 11.07.08 pm.png




If you look at the video in the link (below) Tony Lewis describes how it can run on a watch battery at approx 2.05 mins.


 
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7für7

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Is this something we should keep an eye on it ?


 
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View attachment 81595



If you look at the video in the link (below) Tony Lewis describes how it can run on a watch battery at approx 2.05 mins.



AKIDA 2.0 IP (E, S and P) is available with from 1 to 256 nodes..

So without stating the size of the network on the FPGA, it's a bit hard to make performance comparisons?..

I guess it doesn't "really" matter, as long as it can do it and it's likely to be in the P range (8- 256 nodes).

But it would be nice to know maybe, how many they are actually using, to get that performance?..
 
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manny100

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I read this as one sentence -
Qualcomm wants Brainchip.
Who knows.
One thing we do know is that Snapdragon is not Neuromorphic.
It is not event based but has work arounds which still chew up power.
Does not have on chip learning either.
 
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