BRN (ASX) TENNS

Originally posted by FullMoonFever: ↑

This was something I posted early Mar on Mamba only due to picking up link with Rudy at BRN playing with it.

Saw a couple of our employees "liking" mamba and wondered what it was.

Couple snips below and curious whether it was a general "like", great development thing or whether it is something they are now working on too.

Is possible the PeaBrane/Mamba-Tiny on Git and liked is something that Rudy created by the looks from the full Mamba but maybe I'm reading it wrong?

Interesting none the less I think given the Ai in 24 links Mamba, Transformers & Neuromorphic all together.

AI in 2024 – On an Exponential Rise: Data, Mamba, and More | YouTube inside
Discover the transformative potential of AI in 2024. Dive into key drivers like data quality and the groundbreaking Mamba architecture.
meta-quantum.today

Mamba:This refers to the emergence of new, ground breaking AI architectures like transformers and neuromorphic computing. These architectures mimic the human brain’s structure and function, allowing for significantly faster processing and deeper learning capabilities. Mamba-based models will revolutionize areas like natural language processing, image recognition, and robotics.

Mamba Architecture: Revolutionizing Sequence Modelling
Mamba, a ground breaking architecture, represents a leap forward from the Transformer models.
It addresses the computational challenges of large-scale sequence processing.
Albert Goo’s work on structured state spaces inspired Mamba’s development.
The architecture’s potential lies in its ability to handle extensive sequences, as demonstrated in DNA classification tasks.
[2312.00752] Mamba: Linear-Time Sequence Modeling with Selective State Spaces

[Submitted on 1 Dec 2023]
Mamba: Linear-Time Sequence Modelling with Selective State Spaces
Albert Gu,Tri Dao

Keith Johnson - BrainChip | LinkedIn
I have a passion for machine learning and artificial intelligence. I'm interested in… · Experience: BrainChip · Education: The University of Western Australia · Location: Greater Perth Area · 195 connections on LinkedIn. View Keith Johnson’s profile on LinkedIn, a professional community of 1...
au.linkedin.com

https://www.linkedin.com/in/nolan-ardolino-334b83105

Rudy Pei - BrainChip | LinkedIn
I have a passion for ML research and engineering. In particular, I love efficient models… · Experience: BrainChip · Education: University of California San Diego · Location: San Diego · 500+ connections on LinkedIn. View Rudy Pei’s profile on LinkedIn, a professional community of 1 billion members.
www.linkedin.com

Rudy Pei
Physicist | ML researcher | quantum & neuromorphic computing | behavioral economics | composer
3w Edited

Mamba is a new state-space model out-performing transformers on "everywhere tried". Originally, it was trained with associative scan, which pytorch does not support natively, hence the need for custom CUDA kernels. However, there is a simple math trick to express the associative scans used in mamba as a ratio of two cumulative sums. This makes for an efficient native pytorch implementation of mamba possible. How? Check out my simple repo with an one-file implementation of this idea forking from the mamba-minimal repohttps://lnkd.in/g5QR7yHC #mamba #llm

GitHub - PeaBrane/mamba-tiny: Simple, minimal implementation of the Mamba SSM in one file of PyTorch. More efficient than the minimalist version but less efficient than the original mamba implementation.
github.com

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The Carnegie Mellon University connection is interesting as is the fact that Mamba claimed State of the Art performance at date of publication now of course exceeded by TENNS according to the results published by Dr. Anthony Lewis and team from Brainchip and by some magnitude.

My opinion only DYOR

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