Fast-SNN: Fast Spiking Neural Network by Converting Quantized ANN
Author:
Affiliation:
1. College of Computer Science and Technology, Zhejiang University, Hangzhou, Zhejiang, China
2. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore
Funder
STI 2030 Major Projects
National Natural Science Foundation of China
Key Research and Development Program of Zhejiang Province in China
Zhejiang Lab
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Applied Mathematics,Artificial Intelligence,Computational Theory and Mathematics,Computer Vision and Pattern Recognition,Software
Link
http://xplorestaging.ieee.org/ielx7/34/10308548/10254579.pdf?arnumber=10254579
Reference78 articles.
1. Mapping from Frame-Driven to Frame-Free Event-Driven Vision Systems by Low-Rate Rate Coding and Coincidence Processing--Application to Feedforward ConvNets
2. VirTex: Learning Visual Representations from Textual Annotations
3. Progressive Tandem Learning for Pattern Recognition with Deep Spiking Neural Networks
4. ImageNet Large Scale Visual Recognition Challenge
5. Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing
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