Optimizing the energy consumption of spiking neural networks for neuromorphic applications
This work addresses energy efficiency for neuromorphic computing, but it appears incremental as it builds on existing conversion methods.
The paper tackles the problem of reducing energy consumption in spiking neural networks for neuromorphic applications by proposing an optimization strategy that maintains accuracy, demonstrating results on MNIST-DVS and CIFAR-10 datasets.
In the last few years, spiking neural networks have been demonstrated to perform on par with regular convolutional neural networks. Several works have proposed methods to convert a pre-trained CNN to a Spiking CNN without a significant sacrifice of performance. We demonstrate first that quantization-aware training of CNNs leads to better accuracy in SNNs. One of the benefits of converting CNNs to spiking CNNs is to leverage the sparse computation of SNNs and consequently perform equivalent computation at a lower energy consumption. Here we propose an efficient optimization strategy to train spiking networks at lower energy consumption, while maintaining similar accuracy levels. We demonstrate results on the MNIST-DVS and CIFAR-10 datasets.