Paper Info
Title
An Energy-efficient Reconfigurable Hybrid DNN Architecture for Speech Recognition with Approximate Computing.
Abstract
This paper proposes an hybrid deep neural network (DNN) for speech recognition and an energy-efficient reconfigurable architecture with approximate computing for accelerating the DNN. The hybrid DNN consists of two network models: a binary weight network (BWN) for twenty key words recognition; a recurrent neural network (RNN) for processing acoustic model of high precision common words recognition. To accelerate the hybrid DNN and reduce the energy cost, we propose a digital-analog mixed reconfigurable architecture with approximate computing units, including: a BWN accelerator with analog multi-chain delay-addition units for bit-wise approximate computing, and a RNN accelerator with approximate multiplication units for different calculation accuracy requirements. Implementation and simulation with TSMC 28nm HPC+ process technology, the energy efficiency of proposed architecture can achieves 163.8TOPS/W for twenty key words recognition and 3.3TOPS/W for common words recognition. Comparing with State-of-the-Art architectures, this work achieves over 1.7X better in energy efficiency with approximate computing.
Year
DOI
Venue
2018
10.1109/ICDSP.2018.8631826
DSL
Keywords
Field
DocType
Speech recognition,Energy efficiency,Computer architecture,Computational modeling,Approximate computing,Delays,Recurrent neural networks
Architecture,Computer science,Efficient energy use,Recurrent neural network,Speech recognition,Multiplication,Artificial neural network,Network model,Binary number,Acoustic model
Conference
ISSN
ISBN
Citations 
1546-1874
978-1-5386-6811-5
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Bo Liu165.82
Shisheng Guo200.68
Hai Qin301.01
Yu Gong4127.36
Jinjiang Yang502.37
Wei Ge62111.72
Jun Yang78240.03