BRN Discussion Ongoing

[White Horse]

Hi Bravo.

That poster Flectional who has been showing up on the crappers BRN threads lately sure seems to think Loihi 3 is real.
He comes across as a bit of a tech head having seen him post on WBT and 4DS over the past few years.
He posted on 8/2/26 quoting something called "SapienFusion - Feb 4 - 2026" as a source.

See excerpt of that post below...............

SapienFusion - Feb 4 - 2026

Intel Loihi 3​

Intel’s neuromorphic journey began in 2017 with Loihi 1, continued through Loihi 2’s 2021 release, and culminates in Loihi 3’s January 2026 commercial availability, a processor that represents the most significant architectural departure from conventional computing since GPUs themselves emerged. This isn’t an incremental improvement. This is brain-inspired computing that finally delivers on decades-old promises.
Graded Spikes: Bridging Two Worlds
'Loihi 3’s critical innovation introduces 32-bit graded spikes—a bridge between traditional deep neural networks operating on continuous values and spiking neural networks communicating through discrete events.Earlier neuromorphic generations used binary on/off signaling. A neuron either fired or didn’t. This forced algorithms designed for conventional architectures to undergo complete rewriting. Converting a PyTorch model to a binary spiking neural network required redesigning activation functions, adjusting learning algorithms, tuning temporal dynamics, and accepting accuracy degradation. The result created high barriers to adoption, and most developers stayed with GPUs.Graded spikes solve this problem by encoding information into spike amplitudes across a 32-bit range. Each spike carries nuanced information—not just fire or don’t fire, but fire with this specific intensity. This enables mainstream AI workloads to run on neuromorphic hardware with dramatically reduced power while requiring minimal algorithmic adaptation. Developers can convert existing models with automated tools currently in development, maintain accuracy within 1-2% of original performance, and achieve neuromorphic efficiency without complete redesign. This technical bridge makes commercial viability possible.'

Event-Driven Computation at Scale​

'The power efficiency advantage comes from temporal sparsity—the principle that most neurons remain inactive most of the time, processing only when relevant events occur.GPU processing a video stream at 30 frames per second processes all pixels with full computation for every frame, regardless of whether the scene changes. Frame 2 might be 95% identical to Frame 1, but the GPU performs full computation anyway. Frame 3 might be 97% identical to Frame 2, but again receives full computation. The result delivers massive redundant processing, consuming constant power.'

'Loihi 3 processing the same video stream activates neurons to establish a baseline during the initial scene, then only fires 5% of neurons to detect the changes in Frame 2 when 95% remains unchanged. Frame 3 triggers only 3% of neurons, when 97% stays static. Power consumption becomes proportional to actual information content rather than frame rate.For event-driven sensory data from neuromorphic cameras and event-based audio, Loihi 3 achieves theoretical 1,000× efficiency versus GPUs. This isn’t marketing hyperbole—it’s architectural mathematics. Temporal sparsity with 99% of neurons inactive delivers a 100× reduction. Spatial sparsity through local processing without global synchronization provides a 10× reduction. Combined, these factors multiply to 1,000× efficiency. Real-world performance varies by workload, but event-based applications routinely achieve 500-1,000× improvements.'




good to know where the competition is at - dyor
could always be wrong of course - all freely available in the public domain

Hi Hoppa,
This is a link to the article to which you refer.
https://sapienfusion.com/2026/02/04...omputing-just-ended-nvidias-edge-ai-monopoly/

I have just done a bit of cross-pollination.
I pointed her towards Kevin D. Johnson's project with Akida.

 

[Dijon101]

Brain inspired machines are better at math than expected | ScienceDaily https://share.google/wG1D8I8d7q4gxeZgi

"Brain inspired machines are better at math than expected
Brain-inspired computers just proved they can tackle supercomputer-level math — using a fraction of the energy.
Date:
February 14, 2026
Source:
DOE/Sandia National Laboratories
Summary:
Neuromorphic computers modeled after the human brain can now solve the complex equations behind physics simulations — something once thought possible only with energy-hungry supercomputers. The breakthrough could lead to powerful, low-energy supercomputers while revealing new secrets about how our brains process information."

Not specifically about brainchip, however just further evidence we are invested in the right space.

 

[smoothsailing18]

curlednoodles

To add to this, I found this paper “Robust iterative value conversion: Deep reinforcement learning for neurochip-driven edge robots” (2024).

One author is from MegaChips (Shinya Nishimura, MegaChips Corporation) and others are NAIST-affiliated.
They explicitly use Akida 1000, but call it a neurochip.

MegaChips × NAIST Paper Summary:

It’s a direct MegaChips ↔ NAIST link in print.The author list includes NAIST researchers and a MegaChips Corporation co-author, so this isn’t just corporate website wording — it’s a real joint technical output.
It’s explicitly about SNN robot control on edge hardware. The whole paper is focused on training and running spiking neural network (SNN) policies for battery-limited edge robots using deep reinforcement learning (DRL).
The “neurochip” is named as Akida 1000.In their evaluation, they state the SNN is run on “neurochip (Akida 1000),” which is the key hardware pin that ties this NAIST/MegaChips robotics work to BrainChip/Akida.
They report the exact kind of edge-robot benefit MegaChips would care about.The paper reports ~15× lower power and ~5× faster calculation versus an ARM Cortex-A72 edge CPU baseline — i.e., practical real-time control advantages under tight power constraints.

Conclusion: this paper is strong evidence that MegaChips + NAIST have already implemented SNN-based robot control on Akida 1000, with measured power/speed benefits — which makes MegaChips’ broader NAIST/SNN robotics messaging far more credible.

@manny100@Fact Finder

 

[FuzM]

curlednoodles

To add to this, I found this paper “Robust iterative value conversion: Deep reinforcement learning for neurochip-driven edge robots” (2024).

One author is from MegaChips (Shinya Nishimura, MegaChips Corporation) and others are NAIST-affiliated.
They explicitly use Akida 1000, but call it a neurochip.

MegaChips × NAIST Paper Summary:

It’s a direct MegaChips ↔ NAIST link in print.The author list includes NAIST researchers and a MegaChips Corporation co-author, so this isn’t just corporate website wording — it’s a real joint technical output.
It’s explicitly about SNN robot control on edge hardware. The whole paper is focused on training and running spiking neural network (SNN) policies for battery-limited edge robots using deep reinforcement learning (DRL).
The “neurochip” is named as Akida 1000.In their evaluation, they state the SNN is run on “neurochip (Akida 1000),” which is the key hardware pin that ties this NAIST/MegaChips robotics work to BrainChip/Akida.
They report the exact kind of edge-robot benefit MegaChips would care about.The paper reports ~15× lower power and ~5× faster calculation versus an ARM Cortex-A72 edge CPU baseline — i.e., practical real-time control advantages under tight power constraints.

Conclusion: this paper is strong evidence that MegaChips + NAIST have already implemented SNN-based robot control on Akida 1000, with measured power/speed benefits — which makes MegaChips’ broader NAIST/SNN robotics messaging far more credible.

@manny100@Fact Finder

Video

Paper

 

[suss]

Video

Paper

There are a number of videos on this channel.

 

[FuzM]

There are a number of videos on this channel.

Naist find especially with video as recent as a week ago

 

[7für7]

Hmmm

CES 2026: MIDI Gear to Watch—and the Accessibility Tech That Can Change How We Make Music

CES 2026 ran January 6–9, 2026 in Las Vegas, and once again the show floor was full of ideas that matter to music creators: new ways to control software, new [...]

midi.org

BRAIN INTERFACES AND ON-DEVICE AI (BRAINEULINK + BRAINCHIP)​

BraineuLink describes work on non-invasive EEG brain-computer interfaces for decoding user intentions and interfacing with digital devices, while BrainChip describes its Akida neuromorphic processor platform for low-power, real-time edge AI.

MIDI relevance: these are enabling technologies: lower-power on-device perception and alternate input methods are exactly what’s needed for future accessible instruments, adaptive controllers, and context-aware performance rigs.

BraineuLink

Apart from they are from Taiwan and started in 2021 there ain’t much online about the company


View attachment 95126
View attachment 95127

Portfolio | Maxpro Ventures

www.maxproventures.com

At BraineuLink Technology Inc, they are pioneering the development of non-invasive EEG brain-computer interfaces (BCI).



Their advanced systems include cutting-edge algorithms for decoding user intentions and innovative BCI chips.



They are focused on creating intelligent solutions that allow seamless brain interaction with digital devices such as mobile applications and AR glasses, as well as translating EEG signals into text.



Their mission is to make neural interfacing technology accessible and transformative for everyday use.

Why the hat ?

 

[Stable Genius]

Video

Paper

Awesome @FuzM

Thanks for sharing.

@manny100 Based on that video very happy to say I’m wrong about Megachips still testing Akida.

Great to see!

 

[Baron Von Ricta]

Hmmm

CES 2026: MIDI Gear to Watch—and the Accessibility Tech That Can Change How We Make Music

CES 2026 ran January 6–9, 2026 in Las Vegas, and once again the show floor was full of ideas that matter to music creators: new ways to control software, new [...]

midi.org

BRAIN INTERFACES AND ON-DEVICE AI (BRAINEULINK + BRAINCHIP)​

BraineuLink describes work on non-invasive EEG brain-computer interfaces for decoding user intentions and interfacing with digital devices, while BrainChip describes its Akida neuromorphic processor platform for low-power, real-time edge AI.

MIDI relevance: these are enabling technologies: lower-power on-device perception and alternate input methods are exactly what’s needed for future accessible instruments, adaptive controllers, and context-aware performance rigs.

BraineuLink

Apart from they are from Taiwan and started in 2021 there ain’t much online about the company


View attachment 95126
View attachment 95127

Portfolio | Maxpro Ventures

www.maxproventures.com

At BraineuLink Technology Inc, they are pioneering the development of non-invasive EEG brain-computer interfaces (BCI).



Their advanced systems include cutting-edge algorithms for decoding user intentions and innovative BCI chips.



They are focused on creating intelligent solutions that allow seamless brain interaction with digital devices such as mobile applications and AR glasses, as well as translating EEG signals into text.



Their mission is to make neural interfacing technology accessible and transformative for everyday use.

I've been waiting for these for many years. Reckon they are now becoming a reality

 

[LongTermInvestor]

curlednoodles

To add to this, I found this paper “Robust iterative value conversion: Deep reinforcement learning for neurochip-driven edge robots” (2024).

One author is from MegaChips (Shinya Nishimura, MegaChips Corporation) and others are NAIST-affiliated.
They explicitly use Akida 1000, but call it a neurochip.

MegaChips × NAIST Paper Summary:

It’s a direct MegaChips ↔ NAIST link in print.The author list includes NAIST researchers and a MegaChips Corporation co-author, so this isn’t just corporate website wording — it’s a real joint technical output.
It’s explicitly about SNN robot control on edge hardware. The whole paper is focused on training and running spiking neural network (SNN) policies for battery-limited edge robots using deep reinforcement learning (DRL).
The “neurochip” is named as Akida 1000.In their evaluation, they state the SNN is run on “neurochip (Akida 1000),” which is the key hardware pin that ties this NAIST/MegaChips robotics work to BrainChip/Akida.
They report the exact kind of edge-robot benefit MegaChips would care about.The paper reports ~15× lower power and ~5× faster calculation versus an ARM Cortex-A72 edge CPU baseline — i.e., practical real-time control advantages under tight power constraints.

Conclusion: this paper is strong evidence that MegaChips + NAIST have already implemented SNN-based robot control on Akida 1000, with measured power/speed benefits — which makes MegaChips’ broader NAIST/SNN robotics messaging far more credible.

@manny100@Fact Finder

The flow of positive developments lately hasn’t just been steady - it’s been accelerating. And with the AGM approaching, momentum feels like it’s building, not fading.

Now look at the latest Shortman data, ASIC Short Report & ASX Short Data - a noticeable surge in fresh short positions. Seriously… what are they chasing here? A 1-2 cent dip from $0.135? That’s the grand prize?

Because if a single major announcement drops out of nowhere, this doesn’t move 1-2 cents… it reprices. Fast. And violently.

Cast your mind back to January 2022. The share price exploded to an ATH of $2.34 largely on the back of the Mercedes-Benz hype alone.

Today? The foundation is on a completely different level.

We’re talking involvement with the United States Air Force, NASA, Ant61, Onsor - and endorsement from IBM’s Field CTO, who is publicly demonstrating real-world Akida applications. That is not speculation. That is validation. When someone in IBM’s leadership ranks showcases your tech live, the market eventually pays attention.

And let’s not forget - the AKD1500 and AKD2500 physical chips are expected in Q3 2026 (possibly earlier). Physical silicon changes perception. It shifts the narrative from potential to deployment.

So ask yourself: if the market pushed this to $2.34 on hype in 2022… what happens now, with materially stronger fundamentals and real-world validation, if meaningful news lands between now and the AGM?

Shorts are playing for pennies. But they’re exposed to dollars.

Risk absolutely cuts both ways - and right now, the asymmetry looks far more dangerous for anyone betting against it.

Sometimes the biggest moves happen when the crowd least expects them.

 

[Baron Von Ricta]

Today on the ASX it's battle of the Botts for BRN once again. Every time one Bot raises the share price by .25 the other Bot at the exact same time pushes it back down. It's great to be part of a fair system

 

[gilti]

Today on the ASX it's battle of the Botts for BRN once again. Every time one Bot raises the share price by .25 the other Bot at the exact same time pushes it back down. It's great to be part of a fair system

View attachment 95142

Horrorfing but thus crap has been going on for years. It is called "promoting liquidity" according to the ASX.

 

[Bravo]

Since yesterday's post I've been doing a bit more research into Loihi 3 and discovered a very long and detailed blog on Loihi 3 from someone called Dr Shayan Erfanian (see link below).

The profile material I found presents Dr Erfanian as a technology strategist and software/cybersecurity entrepreneur, not obviously a primary neuromorphic researcher ,which I think pushes the blog more toward forecasting rather than authoritative disclosure.

For what it's worth, Dr Erfanian says that we can expect to hear more about Loihi 3 at the Intel Developer Conference in Q2/3 2026.

In another expert he states "the 2-3 year horizon following Loihi 3's launch (2028-2029) will witness substantial industry restructuring as the energy efficiency benefits of neuromorphic computing become widely acknowledged and integrated."

Dr Shayan Erfanian - Technology Mentor & Strategist

University professor and technology strategist. Teaching thousands of students and mentoring startup founders in AI, digital transformation, and business innovation.

shayanerfanian.com

Excerpt 1

Excerpt 2

All of these ruminations about the emergence of Loihi 3 would seem to align with Intel's job advertising published 16 days ago for an AI Software Architect Neuromorhpic Computing.

The job ad says "Now, we're entering an exciting new chapter: transforming these breakthroughs into real-world products that will power the coming era of physical AI systems beyond the reach of GPUs and mainstream AI accelerators." It also states a key responsibility is to "Integrate neuromorphic software into leading robotics, IoT, and sensing frameworks to enable broad ecosystem adoption."



Intel's Job Ad

AI Software Architect – Neuromorphic Computing

Job Details: Job Description: For nearly a decade, Intel's Neuromorphic Computing Lab-together with a global ecosystem of 250+ research groups-has explored architectures, algorithms, and software inspired by the brain's extraordinary efficiency, scalability, and adaptability. Our Loihi series of...

intel.wd1.myworkdayjobs.com

​

The other thing that struck me yesterday about Mike Davies LinkedIn Post 8 months ago on Loihi 3 was how similar it sounded to Akida 2 with regard to features such as LLM's (see below).

I realise that the addressable market is big enough for more than 1 player, but Intel is a behemoth of a player and therefore a pretty significant threat.

I can’t deny feeling quite disappointed that what once looked like a meaningful lead has now narrowed.

In hindsight, relying solely on an IP-only strategy appears to have seriously limited commercial traction. The pivot to physical chips AKD1500 (and now AKD2500) feels, at least to me, like an acknowledgment that licensing alone wasn’t able to convert at the pace required.

Having said that, I can understand the appeal of the IP-only idea because it's capital-light (no wafer commitments, inventory risk, or hardware support burden). But in practice, IP-only seems to work best when there’s already a mature ecosystem, strong reference implementations and customers ready to integrate with confidence. Neuromorphic as a new and disruptive technology would make pure IP much harder to monetise.

The key question now isn’t whether the pivot was necessary but whether it was too late. The risk is that competitors with much deeper resources and broader ecosystems may be able to close the window further.

In the coming months I'll be keeping a very close eye on any primary sources from Intel announcing Loihi 3 specs.

​

 

[smoothsailing18]

I agree it was foolish to think the uptake across the industry to Neuromorphic compute without samples of hardware was an easy rd when clearly it was not the correct decision. The fact our technology is further advanced than most hopefully will get us across the line to profit and mass integration sooner rather than later.
It's a close race moving forward with several different companies hot on our trail, yet we are well on our way which will definitely work in our favour.