TCL: an ANN-to-SNN Conversion with Trainable Clipping Layers
Spiking-neural-networks (SNNs) are promising at edge devices since the event-driven operations of SNNs provides significantly lower power compared to analog-neural-networks (ANNs). Although it is difficult to efficiently train SNNs, many techniques to convert trained ANNs to SNNs have been developed...
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| Veröffentlicht in: | 2021 58th ACM/IEEE Design Automation Conference (DAC) S. 793 - 798 |
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05.12.2021
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| Abstract | Spiking-neural-networks (SNNs) are promising at edge devices since the event-driven operations of SNNs provides significantly lower power compared to analog-neural-networks (ANNs). Although it is difficult to efficiently train SNNs, many techniques to convert trained ANNs to SNNs have been developed. However, after the conversion, a trade-off relation between accuracy and latency exists in SNNs, causing considerable latency in large size datasets such as ImageNet. We present a technique, named as TCL, to alleviate the trade-off problem, enabling the accuracy of 73.87% (VGG-16) and 70.37% (ResNet-34) for ImageNet with the moderate latency of 250 cycles in SNNs. |
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| AbstractList | Spiking-neural-networks (SNNs) are promising at edge devices since the event-driven operations of SNNs provides significantly lower power compared to analog-neural-networks (ANNs). Although it is difficult to efficiently train SNNs, many techniques to convert trained ANNs to SNNs have been developed. However, after the conversion, a trade-off relation between accuracy and latency exists in SNNs, causing considerable latency in large size datasets such as ImageNet. We present a technique, named as TCL, to alleviate the trade-off problem, enabling the accuracy of 73.87% (VGG-16) and 70.37% (ResNet-34) for ImageNet with the moderate latency of 250 cycles in SNNs. |
| Author | Chang, Ik-Joon Ho, Nguyen-Dong |
| Author_xml | – sequence: 1 givenname: Nguyen-Dong surname: Ho fullname: Ho, Nguyen-Dong email: donghn@khu.ac.kr organization: Kyung Hee University – sequence: 2 givenname: Ik-Joon surname: Chang fullname: Chang, Ik-Joon email: ichang@khu.ac.kr organization: Kyung Hee University |
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| Snippet | Spiking-neural-networks (SNNs) are promising at edge devices since the event-driven operations of SNNs provides significantly lower power compared to... |
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| SubjectTerms | ANN-to-SNN conversion Clocks Design automation Limiting Neural networks Spiking Neural Network trainable clipping layer Training |
| Title | TCL: an ANN-to-SNN Conversion with Trainable Clipping Layers |
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