计算机科学
人工智能
人工神经网络
深度学习
卷积神经网络
残余物
尖峰神经网络
模式识别(心理学)
MNIST数据库
深层神经网络
机器学习
作者
Abhronil Sengupta,Yuting Ye,Robert Y. Wang,Chiao Liu,Kaushik Roy
出处
期刊:arXiv: Computer Vision and Pattern Recognition
日期:2018-02-07
被引量:9
摘要
Over the past few years, Spiking Neural Networks (SNNs) have become popular as a possible pathway to enable low-power event-driven neuromorphic hardware. However, their application in machine learning have largely been limited to very shallow neural network architectures for simple problems. In this paper, we propose a novel algorithmic technique for generating an SNN with a deep architecture, and demonstrate its effectiveness on complex visual recognition problems such as CIFAR-10 and ImageNet. Our technique applies to both VGG and Residual network architectures, with significantly better accuracy than the state-of-the-art. Finally, we present analysis of the sparse event-driven computations to demonstrate reduced hardware overhead when operating in the spiking domain.
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