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
Furber S
(2007)
Neural systems engineering.
in Journal of the Royal Society, Interface
Plana L
(2007)
A GALS Infrastructure for a Massively Parallel Multiprocessor
in IEEE Design & Test of Computers
Furber SB
(2007)
Sparse distributed memory using rank-order neural codes.
in IEEE transactions on neural networks
Furber S
(2008)
The Future of Computer Technology and its Implications for the Computer Industry
in The Computer Journal
Brown A
(2009)
Advances in Neuro-Information Processing
Rast A
(2009)
Advances in Neuro-Information Processing
Yang S
(2009)
A Token-Managed Admission Control System for QoS Provision on a Best-Effort GALS Interconnect
in Fundamenta Informaticae
Wu J
(2009)
A Multicast Routing Scheme for a Universal Spiking Neural Network Architecture
in The Computer Journal
Navaridas J
(2009)
Understanding the interconnection network of SpiNNaker
Mukaram Khan (Author)
(2009)
System Level Modelling for SpiNNaker CMP System
Rast A
(2011)
Managing Burstiness and Scalability in Event-Driven Models on the SpiNNaker Neuromimetic System
in International Journal of Parallel Programming
Grymel M
(2011)
A Novel Programmable Parallel CRC Circuit
in IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Khan M
(2011)
Event-driven configuration of a neural network CMP system over an homogeneous interconnect fabric
in Parallel Computing
Patterson C
(2012)
Scalable communications for a million-core neural processing architecture
in Journal of Parallel and Distributed Computing
Navaridas J
(2013)
SpiNNaker: Fault tolerance in a power- and area- constrained large-scale neuromimetic architecture
in Parallel Computing
Furber S
(2016)
Large-scale neuromorphic computing systems.
in Journal of neural engineering
Furber S
(2016)
Brain-inspired computing
in IET Computers & Digital Techniques
Ghasempour M
(2016)
HAPPY
Furber S
(2017)
Microprocessors: the engines of the digital age.
in Proceedings. Mathematical, physical, and engineering sciences
Sugiarto I
(2017)
Optimized task graph mapping on a many-core neuromorphic supercomputer
Sugiarto I
(2017)
Profiling a Many-core Neuromorphic Platform
Sen-Bhattacharya B
(2017)
A Spiking Neural Network Model of the Lateral Geniculate Nucleus on the SpiNNaker Machine.
in Frontiers in neuroscience
Bogdan PA
(2018)
Structural Plasticity on the SpiNNaker Many-Core Neuromorphic System.
in Frontiers in neuroscience
Brown A
(2018)
SpiNNaker: Event-Based Simulation-Quantitative Behavior
in IEEE Transactions on Multi-Scale Computing Systems
Mikaitis M
(2018)
Neuromodulated Synaptic Plasticity on the SpiNNaker Neuromorphic System
in Frontiers in Neuroscience
Van Albada SJ
(2018)
Performance Comparison of the Digital Neuromorphic Hardware SpiNNaker and the Neural Network Simulation Software NEST for a Full-Scale Cortical Microcircuit Model.
in Frontiers in neuroscience
Kynigos M
(2018)
Network-on-chip evaluation for a novel neural architecture
Rhodes O
(2018)
sPyNNaker: A Software Package for Running PyNN Simulations on SpiNNaker.
in Frontiers in neuroscience
Hopkins M
(2018)
Spiking neural networks for computer vision.
in Interface focus
James R
(2018)
Parallel Distribution of an Inner Hair Cell and Auditory Nerve Model for Real-Time Application
in IEEE Transactions on Biomedical Circuits and Systems
Rast AD
(2018)
Behavioral Learning in a Cognitive Neuromorphic Robot: An Integrative Approach.
in IEEE transactions on neural networks and learning systems
Sen-Bhattacharya B
(2018)
Building a Spiking Neural Network Model of the Basal Ganglia on SpiNNaker
in IEEE Transactions on Cognitive and Developmental Systems
Hopkins M
(2020)
Stochastic rounding and reduced-precision fixed-point arithmetic for solving neural ordinary differential equations.
in Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
Rhodes O
(2020)
Real-time cortical simulation on neuromorphic hardware.
in Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
Plana L
(2020)
spiNNlink: FPGA-Based Interconnect for the Million-Core SpiNNaker System
in IEEE Access
Sugiarto I
(2021)
Fine-grained or coarse-grained? Strategies for implementing parallel genetic algorithms in a programmable neuromorphic platform
in TELKOMNIKA (Telecommunication Computing Electronics and Control)
Bogdan PA
(2021)
Towards a Bio-Inspired Real-Time Neuromorphic Cerebellum.
in Frontiers in cellular neuroscience
Peres L
(2022)
Parallelization of Neural Processing on Neuromorphic Hardware
in Frontiers in Neuroscience
Christensen D
(2022)
2022 roadmap on neuromorphic computing and engineering
in Neuromorphic Computing and Engineering
D'Angelo G
(2022)
Event driven bio-inspired attentive system for the iCub humanoid robot on SpiNNaker
in Neuromorphic Computing and Engineering
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. |