A large-scale spiking neural network accelerator for FPGA systems

Authors:
Kit Cheung;Simon R. Schultz;Wayne Luk
Affiliations:
Department of Computing, Imperial College London, UK;Department of Bioengineering, Imperial College London, UK;Department of Computing, Imperial College London, UK
Venue:
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part I
Year:
2012

Citing 5
Cited 2

Analog VLSI and neural systems

Analog VLSI and neural systems
Challenges for large-scale implementations of spiking neural networks on FPGAs

Neurocomputing
The cat is out of the bag: cortical simulations with 109 neurons, 1013 synapses

Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis
Bluehive - A Field-Programable Custom Computing Machine for Extreme-Scale Real-Time Neural Network Simulation

FCCM '12 Proceedings of the 2012 IEEE 20th International Symposium on Field-Programmable Custom Computing Machines
Simple model of spiking neurons

IEEE Transactions on Neural Networks

2013 Special Issue: Event management for large scale event-driven digital hardware spiking neural networks

Neural Networks
FPGA-based biophysically-meaningful modeling of olivocerebellar neurons

Proceedings of the 2014 ACM/SIGDA international symposium on Field-programmable gate arrays

Quantified Score

Hi-index	0.00

Visualization

Abstract

Spiking neural networks (SNN) aim to mimic membrane potential dynamics of biological neurons. They have been used widely in neuromorphic applications and neuroscience modeling studies. We design a parallel SNN accelerator for producing large-scale cortical simulation targeting an off-the-shelf Field-Programmable Gate Array (FPGA)-based system. The accelerator parallelizes synaptic processing with run time proportional to the firing rate of the network. Using only one FPGA, this accelerator is estimated to support simulation of 64K neurons 2.5 times real-time, and achieves a spike delivery rate which is at least 1.4 times faster than a recent GPU accelerator with a benchmark toroidal network.