A Time Multiplexing Architecture for Inter-neuron Communications

Fergal Tuffy, Liam McDaid, Martin McGinnity, Jose Santos, Peter Kelly, Vunfu Wong Kwan, John Alderman

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)
6 Downloads (Pure)

Abstract

This paper presents a hardware implementation of a Time Multiplexing Architecture (TMA) that can interconnect arrays of neurons in an Artificial Neural Network (ANN) using a single metal wire. The approach exploits the relative slow operational speed of the biological system by using fast digital hardware to sequentially sample neurons in a layer and transmit the associated spikes to neurons in other layers. The motivation for this work is to develop minimal area inter-neuron communication hardware. An estimate of the density of on-chip neurons afforded by this approach is presented. The paper verifies the operation of the TMA and investigates pulse transmission errors as a function of the sampling rate. Simulations using the Xilinx System Generator (XSG) package demonstrate that the effect of these errors on the performance of an SNN, pre-trained to solve the XOR problem, is negligible if the sampling frequency is sufficiently high.
Original languageEnglish
Title of host publicationUnknown Host Publication
PublisherSpringer
Pages944-952
Number of pages8
Volume4131
ISBN (Print)978-3-540-38625-4
DOIs
Publication statusPublished - 2006
Event16th International Conference on Artificial Neural Networks - ICANN 2006 - Athens, Greece
Duration: 1 Jan 2006 → …

Conference

Conference16th International Conference on Artificial Neural Networks - ICANN 2006
Period1/01/06 → …

Bibliographical note

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