2025 BrainChip Discussion, page-4015

@jbar039 @StockHound81

We've known (over at the "echo chamber" no less but maybe not in this place) that Carnegie Mellon under John Shen, have been researching, designing and testing C3S since like 2023. And yes, they were using Akida but who knows what other chips / processors they may have been using too. (Refer post link below).

In the context of what PVDM said or Techs posted understanding of what was said, does not mean BRN have developed it and thus why would we need a release?

If could well just be a comment in conversation that "a" C3S (anyones) has been shown to work in research, no more no less.

My original C3S post back in Sept 2023.

I see we are starting to get CMU Grads appearing on the landscape.

Nice to see them getting out there and wish them well with their neuromorphic knowledge to spread....specifically Akida.

Sri Lakshmi Vemulapalli

Research Assistant at Neuromorphic Computer Architecture Lab | ECE Graduate Student at Carnegie Mellon University | Seeking full time positions starting immediately.

Carnegie Mellon UniversityCarnegie Mellon University

Carnegie Mellon University

1 year 1 month

• Teaching Assistant

  Jan 2023 - May 20235 months
  Pittsburgh, Pennsylvania, United States
  Working as a Teaching Assistant for 18698 - Neural Signal Processing.

• Research Assistant

  Aug 2022 - May 202310 months
  Pittsburgh, Pennsylvania, United States
  Working as a Research Assistant under Prof. John Shen on a Neural Processor, “Akida” by Brainchip.
  - Developing a C3S designs in MetaTF and map to Akida chip.
  - Developing a technique for the conversion of CNNs to Spiking Neural Networks (SNN) and designing a framework for Native TNNs.

In case you wondered what C3S is....

Cortical Columns Computing Systems: Microarchitecture Model, Functional Building Blocks, and Design Tools

Reverse-engineering the human brain has been a grand challenge for researchers in machine learning, experimental neuroscience, and computer architecture. Current deep neural networks (DNNs), motivated by the same challenge, have achieved remarkable results in Machine Learning applications...

www.intechopen.com

Cortical Columns Computing Systems: Microarchitecture Model, Functional Building Blocks, and Design Tools

WRITTEN BY
John Paul Shen and Harideep Nair

Abstract

Reverse-engineering the human brain has been a grand challenge for researchers in machine learning, experimental neuroscience, and computer architecture. Current deep neural networks (DNNs), motivated by the same challenge, have achieved remarkable results in Machine Learning applications. However, despite their original inspiration from the brain, DNNs have largely moved away from biological plausibility, resorting to intensive statistical processing on huge amounts of data. This has led to exponentially increasing demand on hardware compute resources that is quickly becoming economically and technologically unsustainable. Recent neuroscience research has led to a new theory on human intelligence, that suggests Cortical Columns (CCs) as the fundamental processing units in the neocortex that encapsulate intelligence. Each CC has the potential to learn models of complete objects through continuous predict-sense-update loops. This leads to the overarching question:Can we build Cortical Columns Computing Systems (C3S) that possess brain-like capabilities as well as brain-like efficiency? This chapter presents ongoing research in the Neuromorphic Computer Architecture Lab (NCAL) at Carnegie Mellon University (CMU) focusing on addressing this question. Our initial findings indicate that designing truly intelligent and extremely energy-efficient C3S-based sensory processing units, using off-the-shelf digital CMOS technology and tools, is quite feasible and very promising, and certainly warrants further research exploration.