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IJCNN
2007
IEEE

Compact hardware for real-time speech recognition using a Liquid State Machine

9 years 9 months ago
Compact hardware for real-time speech recognition using a Liquid State Machine
Abstract— Hardware implementations of Spiking Neural Networks are numerous because they are well suited for implementation in digital and analog hardware, and outperform classic neural networks. This work presents an application driven digital hardware exploration where we implement realtime, isolated digit speech recognition using a Liquid State Machine (a recurrent neural network of spiking neurons where only the output layer is trained). First we test two existing hardware architectures, but they appear to be too fast and thus area consuming for this application. Then we present a scalable, serialised architecture that allows a very compact implementation of spiking neural networks that is still fast enough for real-time processing. This work shows that there is actually a large hardware design space of Spiking Neural Network hardware that can be explored. Existing architectures only spanned part of it.
Benjamin Schrauwen, Michiel D'Haene, David Verstra
Added 03 Jun 2010
Updated 03 Jun 2010
Type Conference
Year 2007
Where IJCNN
Authors Benjamin Schrauwen, Michiel D'Haene, David Verstraeten, Jan M. Van Campenhout
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