Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.3389/fnins.2019.00095 | FRONTIERS IN NEUROSCIENCE |
Keywords | Field | DocType |
spiking neural networks,event-driven neural networks,sparsity,neuromorphic computing,visual recognition | Neuromorphic hardware,Residual,Computer architecture,Computer science,Network architecture,Visual recognition,Artificial intelligence,Spiking neural network,Artificial neural network,Machine learning,Computation | Journal |
Volume | Citations | PageRank |
13 | 29 | 1.02 |
References | Authors | |
18 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Abhronil Sengupta | 1 | 229 | 23.08 |
| Yuting Ye | 2 | 179 | 10.18 |
| Robert Y. Wang | 3 | 544 | 26.88 |
| Chiao Liu | 4 | 29 | 1.02 |
| Kaushik Roy | 5 | 7093 | 822.19 |