BRN Discussion Ongoing

Tothemoon24

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Tothemoon24

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Esq.111

Fascinatingly Intuitive.
Afternoon Chippers ,

Just came upon this at the the other site .

Don't think I have seen this image before .

Interesting none the less.

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Regards,
Esq.
 
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Diogenese

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TasTroy77

Founding Member
Afternoon Chippers ,

Just came upon this at the the other site .

Don't think I have seen this image before .

Interesting none the less.

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Regards,
Esq.
Renesas !
 
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Bravo

If ARM was an arm, BRN would be its biceps💪!
Not sure if this paper has been shared discussing Spiking Neural Networks for autonomous driving .
Brainchip is mentioned;
We look to be a shining star 🌟 against the mentioned competitors
Solid hour of reading : Link below

Abstract​

The rapid progress of autonomous driving (AD) has triggered a surge in demand for safer and more efficient autonomous vehicles, owing to the intricacy of modern urban environments. Traditional approaches to autonomous driving have heavily relied on conventional machine learning methodologies, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs), for tasks such as perception, decision-making, and control. Presently, major companies such as Tesla, Waymo, Uber, and Volkswagen Group (VW) leverage neural networks for advanced perception and autonomous decision-making. However, concerns have been raised about the escalating computational requirements of training these neural models, primarily in terms of energy consumption and environmental impact. In the situation of optimisation and sustainability, Spiking Neural Networks (SNNs), inspired by the temporal processing of the human brain, have come forth as a third-generation of neural networks, famed for their energy efficiency, potential for handling real-time driving scenarios and processing temporal information efficiently. However, SNNs have not yet achieved the performance levels of their predecessors in critical AD tasks, partly due to the intricate dynamics of neurons, their non-differentiable spike operations, and the lack of specialised benchmark workloads and datasets, among others. This paper examines the principles, models, learning rules, and recent advancements of SNNs in the AD domain. Neuromorphic hardware, hand in hand with SNNs, shows potential but has challenges in accessibility, cost, integration, and scalability. This examination aims to bridge gaps by providing a comprehensive understanding of SNNs in the AD field. It emphasises the role of SNNs in shaping the future of AD while considering optimisation and sustainability.


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Wow @Tothemoon24!

This research paper is really ahead of it's time!😝😜😂

Obviously a quarterly publication.

Please don't anyone go mentioning to the usual suspects on the Crapper that Loihi is, as this paper re-confirms, "still in development" or they might blow a gasket.

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CHIPS

Regular
Hopefully one of their 43 followers, is in need of our tech and has some weight behind them! 😛

Every "little bit" of exposure helps, I guess..

47... she now has 47 followers :D
 
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47... she now has 47 followers :D
Yeah, so I guess that's you and 3 others off of here? 😛

Unless "it's" you, how did you know they are a "she" 🤔..😛
 
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IloveLamp

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Euks

Regular
This would be nice to be us in some way shape or form. I mean come on. We are overdue….. I know Rob’s gone but maybe just maybe….

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Diogenese

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Thanks ILL,

I see INN, the company Meagan works for, has a specific tab at the top of its web page for Artificial Intelligence.

Maybe, as someone said recently, we will start getting traction soon, but the article is dated 5 days ago.

The thing that strikes me about the other 4 is that they could almost be described as one-trick-ponies. They have their niche, and they're very good at it.

I haven't really looked at Appen. We have some overlap with speech recognition and AI models (LLM), but I assume theirs are software.

BRN does have our algorithm product, but I doubt the company is looking to getting into mass market software at this stage. We need to focus our energies on our major EAPs such as Mercedes and Valeo.
 
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7für7

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IloveLamp

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Thanks ILL,

I see INN, the company Meagan works for, has a specific tab at the top of its web page for Artificial Intelligence.

Maybe, as someone said recently, we will start getting traction soon, but the article is dated 5 days ago.

The thing that strikes me about the other 4 is that they could almost be described as one-trick-ponies. They have their niche, and they're very good at it.

I haven't really looked at Appen. We have some overlap with speech recognition and AI models (LLM), but I assume theirs are software.

BRN does have our algorithm product, but I doubt the company is looking to getting into mass market software at this stage. We need to focus our energies on our major EAPs such as Mercedes and Valeo.


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CHIPS

Regular
Yeah, so I guess that's you and 3 others off of here? 😛

Unless "it's" you, how did you know they are a "she" 🤔..😛

Because I am smart and I looked at her LinkedIn profile 🤓 :LOL:. And no, I do not follow her, but I guess some others here do. 😉




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Diogenese

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Bang goes another NDA!!!???
 
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Guzzi62

Regular


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Wow what a nice find!

This Dr Jerry Smith is clearly very knowledgeable on the subject, and I was having a hard time reading his paper, it's over my pay grade, but I understood enough to feel comfortable about my investment here.

He is highlighting us and Tenstorrent and also said their way of working could compliment each other, so not really direct competitors.
 
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Diogenese

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Bang goes another NDA!!!???
Seriously, what does RISC-V have to do with the Akida SoC architecture?

Certainly a partnership was announced with SiFive in April 2022, but that was more about a co-processor arrangement.

https://brainchip.com/brainchip-sifive-partner-deploy-ai-ml-at-edge/

BrainChip and SiFive Partner to Deploy AI/ML Technology at the Edge​

Laguna Hills, Calif. – April 5, 2022 BrainChip Holdings Ltd (ASX: BRN, OTCQX: BRCHF, ADR: BCHPY), the world’s first commercial producer of ultra-low power neuromorphic AI chips and IP, and SiFive, Inc., the founder and leader of RISC-V computing, have combined their respective technologies to offer chip designers optimized AI/ML compute at the edge.
BrainChip’s AkidaTM is a revolutionary advanced neural networking processor architecture that brings AI to the edge in a way that existing technologies are not capable, with high performance, ultra-low power, and on-chip learning. SiFive Intelligence™ solutions with their highly configurable multi-core, multi-cluster capable design, integrate software and hardware to accelerate AI/ML applications. The integration of BrainChip’s Akida technology and SiFive’s multi-core capable RISC-V processors will provide a highly efficient solution for integrated edge AI compute.
SiFive Intelligence™-based processors offer industry leading performance and efficiency for AI and ML workloads. The highly configurable multi-core, multi-cluster capable design has been optimized for the broadest range of applications requiring high-throughput, single-thread performance while under the tightest power and area constraints
.


RISC-V is 32/64 bits

Still, it's better to be talked about than ignored.
 
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Seriously, what does RISC-V have to do with the Akida SoC architecture?

Certainly a partnership was announced with SiFive in April 2022, but that was more about a co-processor arrangement.

https://brainchip.com/brainchip-sifive-partner-deploy-ai-ml-at-edge/

BrainChip and SiFive Partner to Deploy AI/ML Technology at the Edge​

Laguna Hills, Calif. – April 5, 2022 BrainChip Holdings Ltd (ASX: BRN, OTCQX: BRCHF, ADR: BCHPY), the world’s first commercial producer of ultra-low power neuromorphic AI chips and IP, and SiFive, Inc., the founder and leader of RISC-V computing, have combined their respective technologies to offer chip designers optimized AI/ML compute at the edge.
BrainChip’s AkidaTM is a revolutionary advanced neural networking processor architecture that brings AI to the edge in a way that existing technologies are not capable, with high performance, ultra-low power, and on-chip learning. SiFive Intelligence™ solutions with their highly configurable multi-core, multi-cluster capable design, integrate software and hardware to accelerate AI/ML applications. The integration of BrainChip’s Akida technology and SiFive’s multi-core capable RISC-V processors will provide a highly efficient solution for integrated edge AI compute.
SiFive Intelligence™-based processors offer industry leading performance and efficiency for AI and ML workloads. The highly configurable multi-core, multi-cluster capable design has been optimized for the broadest range of applications requiring high-throughput, single-thread performance while under the tightest power and area constraints
.


RISC-V is 32/64 bits

Still, it's better to be talked about than ignored.
I think it's possible, that he's just confused and thinks AKIDA is RISC-V based?

There's not really any other explanation..
 
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Tick tock Sean 🕰️⏰ it’s almost 5 minutes to midnight
 
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