Mixed-signal VLSI neural network based on Continuous Valued Number System
详细信息 查看全文
- 作者：Babak Zamanlooy ; zamanlo@uwindsor.caAuthor Vitae ; Mitra Mirhassani mitramir@uwindsor.caAuthor Vitae
- 关键词：Mixed signal neural networks ; VLSI implementation ; Continuous Valued Number System ; Analog digits
- 刊名：Neurocomputing
- 出版年：2017
- 出版时间：19 January 2017
- 年：2017
- 卷：221
- 期：Complete
- 页码：15-23
- 全文大小：3338 K
- 卷排序：221
文摘
Mixed-signal neural networks have higher energy efficiency and lower area consumption when compared with their equivalent digital implementations. However, the signal processing precision in mixed-signal implementations is limited. A mixed-signal arithmetic method called Continuous Valued Number Systems (CVNS) is employed for development and implementation of different functions required in a neuronal network. Analog digits of this number system enable the higher accuracy of analog operations, and can efficiently relate with digital and binary values. In this paper, design and implementation of a 2-2-1 mixed-signal CVNS network structure is presented, to confirm the developed arithmetic method. In the proposed structure, weights are stored in the digital registers while the arithmetic is based on the CVNS. The CVNS features have been exploited to address the limited signal processing precision issue, which requires resolutions higher than 12-bit for online training of the network. As a result, the proposed structure satisfies this requirement of neural networks, while yielding an optimized network configuration. The proposed network is designed, fabricated, and tested in 0.18μm technology.