Efficient Deployment of Spiking Neural Networks on SpiNNaker Neuromorphic Platform

Spiking Neural Networks (SNNs) have emerged as serious competitors of the traditional Convolutional Neural Networks (CNNs), as they unlock new potential of implementing less complex and more energy efficient neural networks. Current deep CNNs can be converted to SNNs for fast deployment on neuromorphic devices, however existing methods do not investigate the impact of hardware-related parameters that directly affect the accuracy of an SNN. In this brief, we target the SpiNNaker neuromorphic platform and we demonstrate a fast exploration framework that effectively decides the configuration of the target board, in order to achieve the highest possible accuracy. Experimental results show that our method reaches 98.85% SNN accuracy on MNIST dataset, while reducing the exploration time by a factor of 3x compared to exhaustive search.

Automated summary

SNNs have emerged as serious competitors of traditional CNNs, unlocking new potential for more energy-efficient neural networks. A fast exploration framework targeting the SpiNNaker neuromorphic platform has been demonstrated, achieving 98.85% SNN accuracy on the MNIST dataset while reducing exploration time by a factor of 3x.