BRN Discussion Ongoing

Rach2512

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Sorry if already posted.



 
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Rach2512

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This is a great example of brn and we're we soon should hear of a consumer product commercially available with Akida inside, this year would be great 👍.
 
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Tothemoon24

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IMG_2098.jpeg



Neuromorphic Computing: The Chip Architecture Flying Under Everyone's Radar

While the AI industry races to build bigger language models, there's a quieter revolution happening that could reshape how we deploy intelligence at the edge.

Neuromorphic chips work like brains. Instead of crunching data on fixed clock cycles, they fire "spikes" only when something changes, just like neurons.
The result?
100 to 1000x better energy efficiency than GPUs for real-time pattern recognition and sensor processing.

January 2026 marks a significant milestone. Intel's Loihi 3 launched with 8 million neurons on a 4nm process, running at 1.2 watts versus 300+ watts for comparable GPU systems. IBM's NorthPole research chip demonstrated 25x the energy efficiency of Nvidia's H100 for image recognition tasks. BrainChip's Akida has moved from NASA evaluation programs (since 2020) to commercial licensing for space applications through Frontgrade Gaisler and defense systems via Parsons (October 2025).

The real gains show up in specialized applications. Event-based vision systems react in 3 milliseconds instead of the typical 33ms frame delay. Industrial robotics and drones are achieving dramatic improvements in battery efficiency. Research programs with BMW and automotive suppliers are testing neuromorphic processors for faster perception tasks.

This matters because it tackles AI's sustainability problem.

By 2026, AI is projected to consume up to 134 terawatt-hours annually (roughly Sweden's total energy use). Neuromorphic computing addresses this while enabling always-on intelligence in wearables, autonomous systems, and industrial IoT without constant cloud connections.

The challenges? Software development requires "thinking in spikes" rather than traditional programming. Tools like Intel's Lava framework are maturing but still evolving.

These chips haven't hit consumer devices yet. The focus remains on industrial, defense, and aerospace pilots.

In 2 or 3 years, this technology will likely move from specialized applications into AR glasses, medical wearables, and possibly consumer devices. Right now, while everyone focuses on scaling up, neuromorphic computing is quietly making AI sustainable and deployable at the edge.

Worth watching.

#EdgeAI #NeuromorphicComputing #SustainableTech #AI #Innovation

IMG_2099.jpeg
 
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7für7

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View attachment 94562


Neuromorphic Computing: The Chip Architecture Flying Under Everyone's Radar

While the AI industry races to build bigger language models, there's a quieter revolution happening that could reshape how we deploy intelligence at the edge.

Neuromorphic chips work like brains. Instead of crunching data on fixed clock cycles, they fire "spikes" only when something changes, just like neurons.
The result?
100 to 1000x better energy efficiency than GPUs for real-time pattern recognition and sensor processing.

January 2026 marks a significant milestone. Intel's Loihi 3 launched with 8 million neurons on a 4nm process, running at 1.2 watts versus 300+ watts for comparable GPU systems. IBM's NorthPole research chip demonstrated 25x the energy efficiency of Nvidia's H100 for image recognition tasks. BrainChip's Akida has moved from NASA evaluation programs (since 2020) to commercial licensing for space applications through Frontgrade Gaisler and defense systems via Parsons (October 2025).

The real gains show up in specialized applications. Event-based vision systems react in 3 milliseconds instead of the typical 33ms frame delay. Industrial robotics and drones are achieving dramatic improvements in battery efficiency. Research programs with BMW and automotive suppliers are testing neuromorphic processors for faster perception tasks.

This matters because it tackles AI's sustainability problem.

By 2026, AI is projected to consume up to 134 terawatt-hours annually (roughly Sweden's total energy use). Neuromorphic computing addresses this while enabling always-on intelligence in wearables, autonomous systems, and industrial IoT without constant cloud connections.

The challenges? Software development requires "thinking in spikes" rather than traditional programming. Tools like Intel's Lava framework are maturing but still evolving.

These chips haven't hit consumer devices yet. The focus remains on industrial, defense, and aerospace pilots.

In 2 or 3 years, this technology will likely move from specialized applications into AR glasses, medical wearables, and possibly consumer devices. Right now, while everyone focuses on scaling up, neuromorphic computing is quietly making AI sustainable and deployable at the edge.

Worth watching.

#EdgeAI #NeuromorphicComputing #SustainableTech #AI #Innovation

View attachment 94563
Why no link?

Very easy… easier than screenshotting and post everything one by one…

 

TheDon

Regular
View attachment 94562


Neuromorphic Computing: The Chip Architecture Flying Under Everyone's Radar

While the AI industry races to build bigger language models, there's a quieter revolution happening that could reshape how we deploy intelligence at the edge.

Neuromorphic chips work like brains. Instead of crunching data on fixed clock cycles, they fire "spikes" only when something changes, just like neurons.
The result?
100 to 1000x better energy efficiency than GPUs for real-time pattern recognition and sensor processing.

January 2026 marks a significant milestone. Intel's Loihi 3 launched with 8 million neurons on a 4nm process, running at 1.2 watts versus 300+ watts for comparable GPU systems. IBM's NorthPole research chip demonstrated 25x the energy efficiency of Nvidia's H100 for image recognition tasks. BrainChip's Akida has moved from NASA evaluation programs (since 2020) to commercial licensing for space applications through Frontgrade Gaisler and defense systems via Parsons (October 2025).

The real gains show up in specialized applications. Event-based vision systems react in 3 milliseconds instead of the typical 33ms frame delay. Industrial robotics and drones are achieving dramatic improvements in battery efficiency. Research programs with BMW and automotive suppliers are testing neuromorphic processors for faster perception tasks.

This matters because it tackles AI's sustainability problem.

By 2026, AI is projected to consume up to 134 terawatt-hours annually (roughly Sweden's total energy use). Neuromorphic computing addresses this while enabling always-on intelligence in wearables, autonomous systems, and industrial IoT without constant cloud connections.

The challenges? Software development requires "thinking in spikes" rather than traditional programming. Tools like Intel's Lava framework are maturing but still evolving.

These chips haven't hit consumer devices yet. The focus remains on industrial, defense, and aerospace pilots.

In 2 or 3 years, this technology will likely move from specialized applications into AR glasses, medical wearables, and possibly consumer devices. Right now, while everyone focuses on scaling up, neuromorphic computing is quietly making AI sustainable and deployable at the edge.

Worth watching.

#EdgeAI #NeuromorphicComputing #SustainableTech #AI #Innovation

View attachment 94563
In my opinion, even Intel and IBM will eventually have to buy our IP license because BRN is the one and only first ready for deployment. The rest is research only and way too far from being ready for deployment. So, those who are waiting patiently for BRN to succeed, brace yourselves because it will be like winning a lottery.

TheDon
 
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In my opinion, even Intel and IBM will eventually have to buy our IP license because BRN is the one and only first ready for deployment. The rest is research only and way too far from being ready for deployment. So, those who are waiting patiently for BRN to succeed, brace yourselves because it will be like winning a lottery.

TheDon
1769406934371.gif
 
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Guzzi62

Regular
View attachment 94562


Neuromorphic Computing: The Chip Architecture Flying Under Everyone's Radar

While the AI industry races to build bigger language models, there's a quieter revolution happening that could reshape how we deploy intelligence at the edge.

Neuromorphic chips work like brains. Instead of crunching data on fixed clock cycles, they fire "spikes" only when something changes, just like neurons.
The result?
100 to 1000x better energy efficiency than GPUs for real-time pattern recognition and sensor processing.

January 2026 marks a significant milestone. Intel's Loihi 3 launched with 8 million neurons on a 4nm process, running at 1.2 watts versus 300+ watts for comparable GPU systems. IBM's NorthPole research chip demonstrated 25x the energy efficiency of Nvidia's H100 for image recognition tasks. BrainChip's Akida has moved from NASA evaluation programs (since 2020) to commercial licensing for space applications through Frontgrade Gaisler and defense systems via Parsons (October 2025).

The real gains show up in specialized applications. Event-based vision systems react in 3 milliseconds instead of the typical 33ms frame delay. Industrial robotics and drones are achieving dramatic improvements in battery efficiency. Research programs with BMW and automotive suppliers are testing neuromorphic processors for faster perception tasks.

This matters because it tackles AI's sustainability problem.

By 2026, AI is projected to consume up to 134 terawatt-hours annually (roughly Sweden's total energy use). Neuromorphic computing addresses this while enabling always-on intelligence in wearables, autonomous systems, and industrial IoT without constant cloud connections.

The challenges? Software development requires "thinking in spikes" rather than traditional programming. Tools like Intel's Lava framework are maturing but still evolving.

These chips haven't hit consumer devices yet. The focus remains on industrial, defense, and aerospace pilots.

In 2 or 3 years, this technology will likely move from specialized applications into AR glasses, medical wearables, and possibly consumer devices. Right now, while everyone focuses on scaling up, neuromorphic computing is quietly making AI sustainable and deployable at the edge.

Worth watching.

#EdgeAI #NeuromorphicComputing #SustainableTech #AI #Innovation

View attachment 94563
Quote: The release of Intel’s Loihi 3 in January 2026 represents a massive leap in capacity and architectural sophistication.

WRONG!

The LLM used in the piece is hallucinating, there will be no Loihi 3 this year, crap article.


Someone posted earlier a LinkedIn post about "the brain in your pocket", the poster Gary Kolegraff is without a doubt well-meaning but holds a BS in Business Administration, so he is not the right guy to ask.
 
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TECH

Top 20
Standby.."watch us now"
4C and update about to be made public.

Still to premature, but once again, name another company or any company who is excelling at the edge, with revenue that just blows your mind.

NOT ONE, so hold off on your premature judgement calls of our company's efforts.

We are in the game, balls deep, I am happy to let our business partners make the first call.....they all like engaging with our technology, I just wonder why that is?

Regards....Brain Dead :ROFLMAO::ROFLMAO:
 
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Standby.."watch us now"
4C and update about to be made public.

Still to premature, but once again, name another company or any company who is excelling at the edge, with revenue that just blows your mind.

NOT ONE, so hold off on your premature judgement calls of our company's efforts.

We are in the game, balls deep, I am happy to let our business partners make the first call.....they all like engaging with our technology, I just wonder why that is?

Regards....Brain Dead :ROFLMAO::ROFLMAO:
Seriously are you a up ramper,
Tech the shareholders arent the 1's saying Watch Us Now or saying a annoucement is coming, The scoreboard says no annoucement and nothing to back up Watch us Now
 
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