Title :
Compact hardware for real-time speech recognition using a Liquid State Machine
Author :
Schrauwen, Benjamin ; D´Haene, Michiel ; Verstraeten, David ; Van Campenhout, Jan
Author_Institution :
Ghent Univ., Ghent
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.
Keywords :
computer architecture; neural nets; speech recognition; application driven digital hardware exploration; isolated digit speech recognition; liquid state machine; real-time speech recognition; spiking neural network; Arithmetic; Biological system modeling; Computer networks; Costs; Hidden Markov models; Neural network hardware; Neural networks; Neurons; Reservoirs; Speech recognition;
Conference_Titel :
Neural Networks, 2007. IJCNN 2007. International Joint Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
978-1-4244-1379-9
Electronic_ISBN :
1098-7576
DOI :
10.1109/IJCNN.2007.4371111