A scalable chip multiprocessor for large-scale neural simulation
Lead Research Organisation:
University of Manchester
Department Name: Computer Science
Abstract
Biological brains are highly complex systems whose underlying principles of operation are little understood. We know that they comprise very large numbers of nerve cells - neurons - that interact with each other principally through electrical impulses or spikes, and we have instruments that can show which areas of the brain are more or less active at any time, but we know little about the intermediate levels of brain function. How, for example, are all the details of a complex visual scene encoded in the patterns of neural spikes in the visual cortex? And how do we use those patterns to recognize our family and friends?One way to help understand complex systems is to develop hypotheses of how those systems might work and then to use computers to test those hypotheses. Modelling spiking neurons is computationally very intensive, so a modern PC is capable of modelling a few tens of thousands of neurons in real time using a rather simple model of each neuron. In this research we plan to build a new sort of computer designed specifically for modelling large numbers of neurons in real time. This computer will be based upon large numbers of fairly simple microprocessors that communicate with each other using spike events modelled closely on the way biological neurons communicate. We will use developments in semiconductor technology to enable many microprocessors to be put on a single silicon chip, thereby keeping the cost and power consumption of the computer as low as possible.Our brains keep working despite frequent failures of their component neurons, and this fault-tolerant characteristic is of great interest to engineers who wish to make computers more reliable. So this work has two complementary ultimate goals: to use the computer to understand better how the brain works at the level of spike patterns, and to see if biology can help us see how to build computer systems that continue functioning despite component failures.
Publications
Bogdan PA
(2018)
Structural Plasticity on the SpiNNaker Many-Core Neuromorphic System.
in Frontiers in neuroscience
Brown A
(2009)
Advances in Neuro-Information Processing
Brown A
(2018)
SpiNNaker: Event-Based Simulation-Quantitative Behavior
in IEEE Transactions on Multi-Scale Computing Systems
Christensen D
(2022)
2022 roadmap on neuromorphic computing and engineering
in Neuromorphic Computing and Engineering
Furber S
(2007)
Neural systems engineering.
in Journal of the Royal Society, Interface
Furber S
(2017)
Microprocessors: the engines of the digital age.
in Proceedings. Mathematical, physical, and engineering sciences
Furber S
(2016)
Large-scale neuromorphic computing systems.
in Journal of neural engineering
Furber S
(2016)
Brain-inspired computing
in IET Computers & Digital Techniques
Furber S
(2008)
The Future of Computer Technology and its Implications for the Computer Industry
in The Computer Journal
Furber SB
(2007)
Sparse distributed memory using rank-order neural codes.
in IEEE transactions on neural networks
Description | This project developed the SpiNNaker architecture and prototype. |
Exploitation Route | See BIMPA project details. |
Sectors | Digital/Communication/Information Technologies (including Software),Education,Electronics,Healthcare |
URL | http://apt.cs.manchester.ac.uk/projects/SpiNNaker/ |
Description | To develop the full SpiNNaker machines. |
Sector | Digital/Communication/Information Technologies (including Software),Electronics |
Description | A R M Ltd |
Organisation | Arm Limited |
Country | United Kingdom |
Sector | Private |
Start Year | 2006 |
Description | Silistix Ltd |
Organisation | Silistix Ltd |
Country | United Kingdom |
Sector | Private |
Start Year | 2006 |
Company Name | Cogniscience Ltd |
Description | Owns the SpiNNaker IP |
Impact | Receives royalties on SpiNNaker sales. |