Darwin: a neuromorphic hardware co-processor based on Spiking Neural Networks

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• 作者：Juncheng Shen ; De Ma ; Zonghua Gu ; Ming Zhang…
• 关键词：neuromorphic computing ; Spiking Neural Networks (SNN) ; digital VLSI ; 023401 ; 类脑硬件 ; 脉冲神经网络 ; 时分复用 ; 数字超大规模集成电路 ; 可配置神经网络
• 刊名：SCIENCE CHINA Information Sciences
• 出版年：2016
• 出版时间：February 2016
• 年：2016
• 卷：59
• 期：2
• 页码：1-5
• 全文大小：362 KB
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• 作者单位：Juncheng Shen (1) (3)
  De Ma (2)
  Zonghua Gu (1)
  Ming Zhang (1)
  Xiaolei Zhu (2)
  Xiaoqiang Xu (1)
  Qi Xu (1)
  Yangjing Shen (2)
  Gang Pan (1)
  
  1. College of Computer Science, Zhejiang University, Hangzhou, 310027, China
  3. Institute of VLSI Design, Zhejiang University, Hangzhou, 310027, China
  2. Key Laboratory of RF Circuits and Systems, Ministry of Education, Hangzhou Dianzi University, Hangzhou, 310018, China
  
• 刊物类别：Computer Science
• 刊物主题：Chinese Library of Science
  Information Systems and Communication Service
  
• 出版者：Science China Press, co-published with Springer
• ISSN：1869-1919

文摘

Broadly speaking, the goal of neuromorphic engineering is to build computer systems that mimic the brain. Spiking Neural Network (SNN) is a type of biologically-inspired neural networks that perform information processing based on discrete-time spikes, different from traditional Artificial Neural Network (ANN). Hardware implementation of SNNs is necessary for achieving high-performance and low-power. We present the Darwin Neural Processing Unit (NPU), a neuromorphic hardware co-processor based on SNN implemented with digitallogic, supporting a maximum of 2048 neurons, 20482 = 4194304 synapses, and 15 possible synaptic delays. The Darwin NPU was fabricated by standard 180 nm CMOS technology with an area size of 5 ×5 mm2 and 70 MHz clock frequency at the worst case. It consumes 0.84 mW/MHz with 1.8 V power supply for typical applications. Two prototype applications are used to demonstrate the performance and efficiency of the hardware implementation. Keywords neuromorphic computing Spiking Neural Networks (SNN) digital VLSI