A Reconfigurable and Biologically Inspired Paradigm for Computation Using Network-On-Chip and Spiking Neural Networks

Jim Harkin, Fearghal Morgan, Liam McDaid, Steve Hall, Brian McGinley, Seamus Cawley

Research output: Non-textual formWeb publication/site

Abstract

FPGA devices have emerged as a popular platform for the rapid prototyping of biological Spiking Neural Networks (SNNs)applications, offering the key requirement of reconfigurability. However, FPGAs do not efficiently realise the biologically plausible neuron and synaptic models of SNNs, and current FPGA routing structures cannot accommodate the high levels of interneuron connectivity inherent in complex SNNs. This paper highlights and discusses the current challenges of implementing scalable SNNs on reconfigurable FPGAs. The paper proposes a novel field programmable neural network architecture (EMBRACE),incorporating low-power analogue spiking neurons, interconnected using a Network-on-Chip architecture. Results on the evaluation of the EMBRACE architecture using the XOR benchmark problem are presented, and the performance of thearchitecture is discussed. The paper also discusses the adaptability of the EMBRACE architecture in supporting fault tolerantcomputing.
LanguageEnglish
DOIs
Publication statusPublished - 1 Jun 2009

Fingerprint

Field programmable gate arrays (FPGA)
Neural networks
Neurons
Rapid prototyping
Network architecture
Network-on-chip

Cite this

Harkin, Jim (Author) ; Morgan, Fearghal (Author) ; McDaid, Liam (Author) ; Hall, Steve (Author) ; McGinley, Brian (Author) ; Cawley, Seamus (Author). / A Reconfigurable and Biologically Inspired Paradigm for Computation Using Network-On-Chip and Spiking Neural Networks. [Web publication/site].
@misc{ad0d81429d9d4e639151229b129ced95,
title = "A Reconfigurable and Biologically Inspired Paradigm for Computation Using Network-On-Chip and Spiking Neural Networks",
abstract = "FPGA devices have emerged as a popular platform for the rapid prototyping of biological Spiking Neural Networks (SNNs)applications, offering the key requirement of reconfigurability. However, FPGAs do not efficiently realise the biologically plausible neuron and synaptic models of SNNs, and current FPGA routing structures cannot accommodate the high levels of interneuron connectivity inherent in complex SNNs. This paper highlights and discusses the current challenges of implementing scalable SNNs on reconfigurable FPGAs. The paper proposes a novel field programmable neural network architecture (EMBRACE),incorporating low-power analogue spiking neurons, interconnected using a Network-on-Chip architecture. Results on the evaluation of the EMBRACE architecture using the XOR benchmark problem are presented, and the performance of thearchitecture is discussed. The paper also discusses the adaptability of the EMBRACE architecture in supporting fault tolerantcomputing.",
author = "Jim Harkin and Fearghal Morgan and Liam McDaid and Steve Hall and Brian McGinley and Seamus Cawley",
year = "2009",
month = "6",
day = "1",
doi = "10.1155/2009/908740",
language = "English",

}

A Reconfigurable and Biologically Inspired Paradigm for Computation Using Network-On-Chip and Spiking Neural Networks. Harkin, Jim (Author); Morgan, Fearghal (Author); McDaid, Liam (Author); Hall, Steve (Author); McGinley, Brian (Author); Cawley, Seamus (Author). 2009.

Research output: Non-textual formWeb publication/site

TY - ADVS

T1 - A Reconfigurable and Biologically Inspired Paradigm for Computation Using Network-On-Chip and Spiking Neural Networks

AU - Harkin, Jim

AU - Morgan, Fearghal

AU - McDaid, Liam

AU - Hall, Steve

AU - McGinley, Brian

AU - Cawley, Seamus

PY - 2009/6/1

Y1 - 2009/6/1

N2 - FPGA devices have emerged as a popular platform for the rapid prototyping of biological Spiking Neural Networks (SNNs)applications, offering the key requirement of reconfigurability. However, FPGAs do not efficiently realise the biologically plausible neuron and synaptic models of SNNs, and current FPGA routing structures cannot accommodate the high levels of interneuron connectivity inherent in complex SNNs. This paper highlights and discusses the current challenges of implementing scalable SNNs on reconfigurable FPGAs. The paper proposes a novel field programmable neural network architecture (EMBRACE),incorporating low-power analogue spiking neurons, interconnected using a Network-on-Chip architecture. Results on the evaluation of the EMBRACE architecture using the XOR benchmark problem are presented, and the performance of thearchitecture is discussed. The paper also discusses the adaptability of the EMBRACE architecture in supporting fault tolerantcomputing.

AB - FPGA devices have emerged as a popular platform for the rapid prototyping of biological Spiking Neural Networks (SNNs)applications, offering the key requirement of reconfigurability. However, FPGAs do not efficiently realise the biologically plausible neuron and synaptic models of SNNs, and current FPGA routing structures cannot accommodate the high levels of interneuron connectivity inherent in complex SNNs. This paper highlights and discusses the current challenges of implementing scalable SNNs on reconfigurable FPGAs. The paper proposes a novel field programmable neural network architecture (EMBRACE),incorporating low-power analogue spiking neurons, interconnected using a Network-on-Chip architecture. Results on the evaluation of the EMBRACE architecture using the XOR benchmark problem are presented, and the performance of thearchitecture is discussed. The paper also discusses the adaptability of the EMBRACE architecture in supporting fault tolerantcomputing.

U2 - 10.1155/2009/908740

DO - 10.1155/2009/908740

M3 - Web publication/site

ER -