POETS: A parallel cluster architecture for Spiking Neural Network

Spiking Neural Networks (SNNs) are known as a branch of neuromorphic computing, and are currently used in neu-roscience applications to understand and model the biological brain. SNNs could also potentially be used in many other application domains such as classification, pattern recognition, and au...

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Bibliographic Details
Published in:International journal of machine learning and computing 2020
Main Authors: Shahsavari, Mahyar, Beaumont, Jonathan, Thomas, David, Brown, Andrew
Format: Article
Language:English
Subjects:
Computer Science
Hardware Architecture
Machine Learning
Neural and Evolutionary Computing
Online Access:Get full text
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Summary:Spiking Neural Networks (SNNs) are known as a branch of neuromorphic computing, and are currently used in neu-roscience applications to understand and model the biological brain. SNNs could also potentially be used in many other application domains such as classification, pattern recognition, and autonomous control. This work presents a highly-scalable hardware platform called POETS, and uses it to implement SNN on a very large number of parallel and reconfigurable FPGA-based processors. The current system consists of 48 FPGAs, providing 3072 processing cores and 49152 threads. We use this hardware to implement up to four million neurons with one thousand synapses. Comparison to other similar platforms shows that the current POETS system is twenty times faster than the Brian simulator, and at least two times faster than SpiNNaker. Index Terms-spiking neural networks, Parallel distributed system, reconfigurable architecture.
ISSN:2010-3700