A High Learning Capability Probabilistic Spiking Neural Network Chip

被引：0

作者：

Hsieh, Hung-Yi ^{[1
]}

Li, Pin-Yi ^{[1
]}

Yang, Cheng-Han ^{[1
]}

Tang, Kea-Tiong ^{[1
]}

机构：

[1] Natl Tsing Hua Univ, Dept Elect Engn, Neuromorph & Biomed Engn Lab, Hsinchu 30013, Taiwan

来源：

2018 INTERNATIONAL SYMPOSIUM ON VLSI DESIGN, AUTOMATION AND TEST (VLSI-DAT) | 2018年

关键词：

Online learning; Probabilistic Spiking Neural Network; Switehed-capacitor; Learning capability; IMPLEMENTATION; HARDWARE;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper presents an analog implementation of probabilistic spiking neural network for portable or biomedical applications which require learning or classification. Online learning adjusts weights by spike based computation. The weight is saved in the long-term synaptic memory. The circuit primarily uses the switched-capacitor structures and was fabricated using 0.18 mu m CMOS technology. This chip consumes less than 10 mu W under a 1V supply and the core area of the chip occupies 0.43mm(2). The chip can learn 80 random patterns with the area under curve of 0.8. The result indicates the chip is appropriate for portable or biomedical applications.

引用

页数：4

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