Abstract
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 3× compared to exhaustive search.
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