Energy efficient parallel neuromorphic architectures with approximate arithmetic on FPGA

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• 作者：Qian Wang^a ; ^{qwangku@tamu.edu}Author Vitae ; Youjie Li^a ; ^{lyj2013apply@tamu.edu}Author Vitae ; Botang Shao^b ; ^{jackieshao2011@gmail.com}Author Vitae ; Siddhartha Dey^a ; ^{sidhart.de@email.tamu.edu}Author Vitae ; Peng Li^a ; ^pli@tamu.eduAuthor Vitae
• 关键词：Spiking neural network ; Neuromorphic system ; Approximate computing ; Parallel architecture
• 刊名：Neurocomputing
• 出版年：2017
• 出版时间：19 January 2017
• 年：2017
• 卷：221
• 期：Complete
• 页码：146-158
• 全文大小：3121 K
• 卷排序：221

文摘

In this paper, we present the parallel neuromorphic processor architectures for spiking neural networks on FPGA. The proposed architectures address several critical issues pertaining to efficient parallelization of the update of membrane potentials, on-chip storage of synaptic weights and integration of approximate arithmetic units. The trade-offs between throughput, hardware cost and power overheads for different configurations are thoroughly investigated. Notably, for the application of handwritten digit recognition, a promising training speedup of 13.5x and a recognition speedup of 25.8x are achieved by a parallel implementation whose degree of parallelism is 32. In spite of the 120 MHz operating frequency, the 32-way parallel hardware design demonstrates a 59.4x training speedup over the single-thread software program running on a 2.2 GHz general purpose CPU. Equally importantly, by leveraging the built-in resilience of the neuromorphic architecture we demonstrate the energy benefit resulted from the use of approximate arithmetic computation. Up to 20% improvement in energy consumption is achieved by integrating approximate multipliers into the system while maintaining almost the same level of recognition rate achieved using standard multipliers. To the best of our knowledge, it is the first time that the approximate computing and parallel processing are applied to FPGA based spiking neural networks. The influence of the parallel processing on the benefits of approximate computing is also discussed in detail.