RECONFIGURABLE PLATFORMS AND THE CHALLENGES FOR LARGE-SCALE IMPLEMENTATIONS OF SPIKING NEURAL NETWORKS

被引：0

作者：

Harkin, Jim ^{[1
]}

Morgan, Fearghal ^{[2
]}

Hall, Steve ^{[3
]}

Dudek, Piotr ^{[4
]}

Dowrick, Thomas ^{[3
]}

McDaid, Liam ^{[1
]}

机构：

[1] Univ Ulster, Intelligent Syst Res Ctr, Coleraine BT52 1SA, Londonderry, North Ireland

[2] Natl Univ Ireland Univ Coll Galway, Bio Inspired Elect & Reconfigurable Comp Grp, Galway, Ireland

[3] Univ Liverpool, Inst Nanoscale Sci Engn & Technol, Liverpool, Merseyside, England

[4] Univ Manchester, Sch Elect & Elect Engn, Manchester, Lancs, England

来源：

2008 INTERNATIONAL CONFERENCE ON FIELD PROGRAMMABLE AND LOGIC APPLICATIONS, VOLS 1 AND 2 | 2008年

关键词：

D O I：

暂无

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

FPGA devices have witnessed popularity in their use for the rapid prototyping of biological Spiking Neural Network (SNNs) applications, as they offer the key requirement of reconfigurability. However, FPGAs do not efficiently realise the biological neuron/synaptic models. Also their routing structures cannot accommodate the high levels of neuron inter-connectivity inherent in complex SNNs. This paper highlights and discusses the current challenges of implementing large scale SNNs on reconfigurable FPGAs. The paper presents a novel Field Programmable Neural Network (FPNN) architecture incorporating low power analogue synapse and a network on chip architecture for SNN routing and configuration. Initial results are presented.

引用

页码：482 / +

页数：2

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