Abstract
With increased interests of neural networks, hardware implementations of neural networks have been investigated. Researchers pursue low hardware cost by using different technologies such as stochastic computing and quantization. For example, the quantization is able to reduce total number of trained weights and results in low hardware cost. Stochastic computing aims to lower hardware costs substantially by using simple gates instead of complex arithmetic operations. In this paper, we propose a new stochastic multiplier with shifted unary code adders (SUC-Adder) for quantized neural networks. The new design uses the characteristic of quantized weights and tremendously reduces the hardware cost of neural networks. Experimental results indicate that our stochastic design achieves about 10x energy reduction compared to its counterpart binary implementation while maintaining slightly higher recognition error rates than the binary implementation.
Original language | English (US) |
---|---|
Title of host publication | 2018 19th International Symposium on Quality Electronic Design, ISQED 2018 |
Publisher | IEEE Computer Society |
Pages | 376-382 |
Number of pages | 7 |
ISBN (Electronic) | 9781538612149 |
DOIs | |
State | Published - May 9 2018 |
Event | 19th International Symposium on Quality Electronic Design, ISQED 2018 - Santa Clara, United States Duration: Mar 13 2018 → Mar 14 2018 |
Publication series
Name | Proceedings - International Symposium on Quality Electronic Design, ISQED |
---|---|
Volume | 2018-March |
ISSN (Print) | 1948-3287 |
ISSN (Electronic) | 1948-3295 |
Other
Other | 19th International Symposium on Quality Electronic Design, ISQED 2018 |
---|---|
Country/Territory | United States |
City | Santa Clara |
Period | 3/13/18 → 3/14/18 |
Bibliographical note
Publisher Copyright:© 2018 IEEE.
Keywords
- Multiplier
- Neural networks
- Quantization
- Stochastic computing