Hierarchical Mapping of Large-Scale Spiking Convolutional Neural Networks Onto Resource-Constrained Neuromorphic Processor

Neuromorphic processors have been designed as nonvon Neumann systems for energy-efficient spiking neural network (SNN) execution. Spiking convolutional neural networks (SCNNs), combining the advantage of convolutional neural network (CNN) and SNN, have been widely applied to vision tasks. However, as the scale of SCNNs increases, executing large-scale SCNNs on resource-constrained neuromorphic processor faces many challenges, including massive synapse pruning, caused by resource competition, execution performance degradation, etc. Addressing these problems, we propose an efficient approach to map large-scale SCNNs onto resource-constrined neuromorphic processor. The approach consists of three steps: 1) splitting; 2) partitioning; and 3) mapping. We explore three acyclic splitting strategies to divide large-scale SCNNs into subnetworks without cyclic dependency. Axon sharing is the guiding principle to partition subnetworks into multiple clusters. To obtain an optimal cluster-to-core mapping scheme, we use nondominated sorting genetic algorithm to collaboratively optimize two metrics. We evaluate our approach with eight realistic SCNN applications. The results show that compared with existing state-of-the-art methods, our approach significantly reduces the synapse pruning and accuracy loss, and increases the execution performance.