传感技术学报2017，Vol.30Issue(4)：535-541,7.DOI:10.3969/j.issn.1004-1699.2017.04.009
用于特征提取的小尺寸事件型卷积处理器
A small Size Event-Based Convolution Processor for Feature Extraction
摘要
Abstract
This paper presents a small size event-based convolution processor for feature extraction of the data generated by dynamic vision sensors(DVS).It consists of a 32 pixel×32 pixel array,a RAM array that stores the convolution kernel,a left/right shift block,a control block,and an asynchronous event readout block.In order to reduce the area of convolution chip,a kernel RAM of 32×32 2 bit word is implemented to store the kernels.In each pixel unit,a 7bit counter is used to accomplish the accumulation instead of a traditional accumulator for smaller pixel size.In the 0.18 μm CMOS technology,each convolution unit occupies 37.5 μm×40 μm.The minimum latency between input and output event flows can be nearly 17 ns.Input event throughput can reach 12.5 Meps.Furthermore,a categorization system is established based on this convolution module,which consists of 16 assembled convolution modules for feature extraction and a spiking neural networks(SNN)for recognition.The experimental results show that the proposed convolution processor can achieve ideal convolution results.With the recognition system,the experimental results of MNIST show that the convolution module configured with 2 bit kernel weights resolution can also complete the feature extraction which achieves a recognition rate of 90.57%.关键词
动态视觉传感器/地址事件表示/小尺寸芯片/卷积芯片/MNIST/脉冲神经网络Key words
dynamic vision sensors/address event representation/small chip size/convolution chip/MNIST/spiking neural networks分类
信息技术与安全科学引用本文复制引用
卢成业,高志远,徐江涛..用于特征提取的小尺寸事件型卷积处理器[J].传感技术学报,2017,30(4):535-541,7.基金项目
国家自然科学基金项目(61604107,61434004) （61604107,61434004）
