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Lithionic memristors & future neuromorphic
What role could lithionic memristive technology play in unlocking the full potential of neuromorphic computing.
AKIDA AND ERICSSON
![Smiling face with sunglasses :sunglasses: 😎](https://cdn.jsdelivr.net/joypixels/assets/6.6/png/unicode/64/1f60e.png)
This is the body of the email, I just sent Tony Dawe.
I will let the forum know, if there is a response or information I can post, regarding the subject
"You may have noticed, there has been some discussion on TSEx about Dr Tony Lewis's LinkedIn comment, concerning small LMs and neuromorphic hardware.
His comments, in my opinion, were ambiguous.
He said both, neuromorphic hardware using them and the VSLMs themselves "hold promise".
Also stating, to his knowledge, BrainChip would be the first to implement this, at the Edge.
To me, he said they are still working on it, but it could also be, that he, being commercially minded, was saying..
"We've done it, but AKD2000 doesn't exist yet, so it's still just in simulation".
In FactFinder's post, about the November 6th private shareholder meeting, he stated the following.
"It has been confirmed by the CEO Sean Hehir in person to myself and others that Brainchip has succeeded in developing and running a number of Large Language Models, LLMs, on AKIDA 2.0 and that AKIDA 3.0 is on track and that its primary purpose will be to focus on running LLM's as its point of technology difference".
Straight off the bat, FactFinder refers to LLMs, not the "very small" or "tiny" LMs, that Dr Tony, refers to having promise.
So either he misquoted Sean, or Sean referenced LLMs.
From the above meeting, it appears that, at the very least, very small LMs are successfully running, in simulation, on AKIDA 2.0 and this is now public knowledge?
My questions are..
Are VSLMs running successfully on AKIDA 2.0, or are they still ironing out the bugs and this will be more an AKIDA 3.0 focused, game?
If they are running successfully, this would be considered quite a huge achievement (being a World first and considering the current hunger, for such technology).
Why hasn't the Company made a proper statement/Tweet, or something, when the information is apparently public knowledge?
It seems to me, that it would be in the Company's best interest (as well as us shareholders of course) to at least "tap" the drum and not have to rely on Chinese whispers etc?"
Nothing to share yet, unless I want to get nasty..
No, I'm not a Janet Jackson fan![]()
As ever, it's always gratifying when one's doodle entendres do not pass unremarked.What? You sleep with your tailor??
SC
"Neuromorphic computing is a technology that even companies like IBM and Intel have not been able to implement, and we are proud to be the first in the world to run the LLM with a low-power neuromorphic accelerator," Yoo said.View attachment 58597
Hi @DingoBorat,
it is a shame your email appears not to have been responded to in a satisfying manner by our company. Hopefully someone at Brainchip will follow up soon, though, with the content of the article below surely sending shock waves through the neuromorphic hardware community right now!
The tech is way over my head, but it looks as if researchers at the Korea Advanced Institute of Science and Technology (KAIST) have found a way to run LLMs on edge devices after all and were also the first in the world to publicly demonstrate and announce their success.
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KAIST develops human brain-like AI chip
Researchers at the Korea Advanced Institute of Science and Technology (KAIST) have developed an AI semiconductor capable of processing large language model (LLM) data at ultra-high speeds while significantly reducing power consumption, according to the Ministry of Science and ICT.m.koreatimes.co.kr
Business
2024-03-06 16:31
KAIST develops human brain-like AI chip
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Yoo Hoi-jun, center, a KAIST professor, and Kim Sang-yeob, left, a member of Yoo's research team, demonstrate a neuromorphic AI semiconductor that uses computing technology mimicking the behavior of the human brain at the ICT ministry's headquarters in Sejong, Wednesday. Yonhap
By Baek Byung-yeul
Researchers at the Korea Advanced Institute of Science and Technology (KAIST) have developed an AI semiconductor capable of processing large language model (LLM) data at ultra-high speeds while significantly reducing power consumption, according to the Ministry of Science and ICT.
The ICT ministry said Wednesday that a research team led by professor Yoo Hoi-jun at KAIST's processing-in-memory research center developed the world's first complementary-transformer AI chip using Samsung Electronics' 28-nanometer manufacturing process.
The complementary-transformer AI chip is a neuromorphic computing system that mimics the structure and function of the human brain. Utilizing a deep learning model commonly used in visual data processing, the research team successfully implemented this transformer function, gaining insights into how neurons process information.
This technology, which learns context and meaning by tracking relationships within data, such as words in a sentence, is a source technology for generative AI services like ChatGPT, the ministry said.
The research team demonstrated the functionality of the complementary-transformer AI chip at the ICT ministry's headquarters in Sejong on Wednesday.
Kim Sang-yeob, a member of the research team, conducted various tasks such as sentence summarization, translation and question-and-answer tasks using OpenAI's LLM, GPT-2, on a laptop equipped with a built-in complementary-transformer AI chip, all without requiring an internet connection. As a result, the performance was notably enhanced, with the tasks completed at least three times faster, and in some cases up to nine times faster, compared to running GPT-2 on an internet-connected laptop.
To implement LLMs typically utilized in generative AI tasks, a substantial number of graphic processing units (GPUs) and 250 watts of power are typically required. However, the KAIST research team managed to implement the language model using a compact AI chip measuring just 4.5 millimeters by 4.5 millimeters.
"Neuromorphic computing is a technology that even companies like IBM and Intel have not been able to implement, and we are proud to be the first in the world to run the LLM with a low-power neuromorphic accelerator," Yoo said.
He predicted this technology could emerge as a core component for on-device AI, facilitating AI functions to be executed within a device even without requiring an internet connection. Due to its capacity to process information within devices, on-device AI offers faster operating speed and lower power consumption compared to cloud-based AI services that rely on network connectivity.
"Recently, with the emergence of generative AI services like ChatGPT and the need for on-device AI, demand and performance requirements for AI chips are rapidly increasing. Our main goal is to develop innovative AI semiconductor solutions that meet these changing market needs. In particular, we aim to focus on research that identifies and provides solutions to additional problems that may arise during the commercialization process," Yoo added.
The research team said this semiconductor uses only 1/625 of the power and is only 1/41 the size of Nvidia's GPU for the same tasks.
Baek Byung-yeul
baekby@koreatimes.co.kr
I guess he doesn't think BrainChip has reached the "Tipping point" yet then?
What's significant here, is that they are talking about running ChatGPT-2 (while although not the current GPT-4, it's still a big deal) on it and not the small to tiny Language Models, that Tony has said we are working on..
They obviously already have a chip, but they're saying they are still research and we don't know what their commercialisation plans are..
Their claim is bold, which BrainChip should refute, if they are in a position to do so.
"Neuromorphic computing is a technology that even companies like IBM and Intel have not been able to implement, and we are proud to be the first in the world to run the LLM with a low-power neuromorphic accelerator," Yoo said.
What's significant here, is that they are talking about running ChatGPT-2 (while although not the current GPT-4, it's still a big deal) on it and not the small to tiny Language Models, that Tony has said we are working on..
They obviously already have a chip, but they're saying they are still research and we don't know what their commercialisation plans are..
Their claim is bold, which BrainChip should refute, if they are in a position to do so.
So sounds like you don't think their tech, is/will be much chop in other areas then..Just to keep things in perspective:
A Short History Of ChatGPT: How We Got To Where We Are Today (forbes.com)
https://www.forbes.com/sites/bernar...we-got-to-where-we-are-today/?sh=2e2b79c2674f
GPT-1, the model that was introduced in June 2018, was the first iteration of the GPT (generative pre-trained transformer) series and consisted of 117 million parameters. This set the foundational architecture for ChatGPT as we know it today. GPT-1 demonstrated the power of unsupervised learning in language understanding tasks, using books as training data to predict the next word in a sentence.
GPT-2, which was released in February 2019, represented a significant upgrade with 1.5 billion parameters. It showcased a dramatic improvement in text generation capabilities and produced coherent, multi-paragraph text. But due to its potential misuse, GPT-2 wasn't initially released to the public. The model was eventually launched in November 2019 after OpenAI conducted a staged rollout to study and mitigate potential risks.
GPT-3 was a huge leap forward in June 2020. This model was trained on a staggering 175 billion parameters. Its advanced text-generation capabilities led to widespread use in various applications, from drafting emails and writing articles to creating poetry and even generating programming code. It also demonstrated an ability to answer factual questions and translate between languages.
When GPT-3 launched, it marked a pivotal moment when the world started acknowledging this groundbreaking technology. Although the models had been in existence for a few years, it was with GPT-3 that individuals had the opportunity to interact with ChatGPT directly, ask it questions, and receive comprehensive and practical responses. When people were able to interact directly with the LLM like this, it became clear just how impactful this technology would become.
GPT-4, the latest iteration, continues this trend of exponential improvement, with changes like:
● Improved model alignment — the ability to follow user intention
● Lower likelihood of generating offensive or dangerous output
● Increased factual accuracy
● Better steerability — the ability to change behavior according to user requests
● Internet connectivity – the latest feature includes the ability to search the Internet in real-time
Each milestone brings us closer to a future where AI seamlessly integrates into our daily lives, enhancing our productivity, creativity, and communication.
Here are a couple of KAIST patent applications:
US2023098672A1 ENERGY-EFFICIENT RETRAINING METHOD OF GENERATIVE NEURAL NETWORK FOR DOMAIN-SPECIFIC OPTIMIZATION 20210924
YOO HOI JUN [KR]; KIM SO YEON [KR]
View attachment 58609
the present invention to provides an energy-efficient retraining method of a generative neural network for domain-specific optimization capable of selecting only some layers of a previously trained generative neural network, i.e. only layers that play a key role in improving retraining performance, at the time of retraining of the generative neural network, and retraining only the selected layers, whereby it is possible to greatly reduce operation burden while maintaining the existing performance.
the mobile device maintains original weights without weight update for unselected layers, after selecting the k continuous layers, does not perform even back propagation for unselected layers before a first one of the selected layers, performs forward propagation in only a first epoch of retraining, and reuses a result of the forward propagation of the first epoch in repeated retraining epochs thereafter.
US2023072432A1 APPARATUS AND METHOD FOR ACCELERATING DEEP NEURAL NETWORK LEARNING FOR DEEP REINFORCEMENT LEARNING 20210831
View attachment 58610
Provided is a deep neural network (DNN) learning accelerating apparatus for deep reinforcement learning, the apparatus including: a DNN operation core configured to perform DNN learning for the deep reinforcement learning; and a weight training unit configured to train a weight parameter to accelerate the DNN learning and transmit it to the DNN operation core, the weight training unit including: a neural network weight memory storing the weight parameter; a neural network pruning unit configured to store a sparse weight pattern generated as a result of performing the weight pruning based on the weight parameter; and a weight prefetcher configured to select/align only pieces of weight data of which values are not zero (0) from the neural network weight memory using the sparse weight pattern and transmit the pieces of weight data of which the values are not zero to the DNN operation core.
While this is an impressive achievement by KAIST,
A. ChatGPT2 is (relatively) small beer.
B. I didn't see any of our secret sauce.
Really nice to see.... how underrated this company is! Watch the business not the stock!The effect of good news on Brainchip share price:
View attachment 58574
And calling GPT2 a LLM, isn't quite accurate, in the context, that even GPT3 has more than ten times the parameters.
I actually sent an email to the Company a couple or so months ago (after someone here, posted an article about a company that had developed an enhanced electric motor design, through the use of Generative A.I.) asking whether they were making use of Generative A.I. to advance developments and problem solve (fearing that pride may make them avoid, or not consider this).Now we know why Peter retired.
Hopkins engineers collaborate with ChatGPT4 to design brain-inspired chips | Hub (jhu.edu)
https://hub.jhu.edu/2024/03/04/chatgpt4-brain-inspired-chips/
HOPKINS ENGINEERS COLLABORATE WITH CHATGPT4 TO DESIGN BRAIN-INSPIRED CHIPS
Systems could power energy-efficient, real-time machine intelligence for next-generation autonomous vehicles, robots 20240303
Through step-by-step prompts to ChatGPT4, starting with mimicking a single biological neuron and then linking more to form a network, they generated a full chip design that could be fabricated.
Fair enough, but I still disagreeIt is accurate, nevertheless, as all members of Open AI’s GPT family qualify as LLMs.
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Size Matters: How Big Is Too Big for An LLM?
Compute-optimal large language models according to the Chinchilla paperpub.towardsai.net
View attachment 58614
View attachment 58615
BrainChip's #neuromorphic tech in Lower Orbit! Akida's journey unfolds, bringing groundbreaking possibilities on earth and beyond. Stay tuned as the story continues to unfold!
Hi McHale,This is hit and run too, Fact Finder just put up a post on HC at 6:02PM this evening post# 72778782 regarding Ericsson 6G zero energy and Akida.
I am having problems with getting links and images up on here at present, hence no charts lately and being time poor means some one else needs to go over to HC and get this very interesting post copied and put up here.
BrainChip's #neuromorphic tech in Lower Orbit! Akida's journey unfolds, bringing groundbreaking possibilities on earth and beyond. Stay tuned as the story continues to unfold!
I believe that the answer to your question is yes and no.....With no news about NEW IP agreement, is it possible that if a Company did take up
an IP agreement but specify a NDA, could this be happening![]()
Great spot!Hi Tech.
Just wondering if you were up for a catch up as I am currently staying in Manganui (pub) for a few days on way up to the 90 mile next week.
I haven't met anyone who is into Brainchip other than friends and family who I coerced into joining me.
I do like you thoughts and input very positive.
Cheers