NeuProMa: A Toolchain for Mapping Large-Scale Spiking Convolutional Neural Networks onto Neuromorphic Processor

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AbstractNeuromorphic processors, the new generation of brain-inspired non-von Neumann computing systems, have the potential to perform complex computations with more energy efficiency than conventional architectures. Neuromorphic processors typically implement the spiking neural network (SNN)-based applications. However, a non-optimized mapping of SNNs onto the neuromorphic processor may increase the on-chip communication delay and data exchange between the off-chip and on-chip memory, especially when the size of the SNNs exceeds the capacity of the processor limited by the on-chip resources. This paper proposes a toolchain, called NeuProMa, to map large-scale spiking convolutional neural networks (SCNNs) onto resource-constrained neuromorphic processors. We exploit the implicit regular connections in the SCNNs and split the SCNNs into multiple sub-networks while reducing the data exchange between the off-chip and on-chip memory. Then, we partition the sub-networks into multiple clusters sequentially in a specific order, which significantly reduces the spike messages between neuromorphic cores. Finally, NeuProMa dispatches the clusters to the neuromorphic cores, minimizing the maximum workload of the routers. Our experiments using six SCNN-based applications show that NeuProMa can significantly reduce the data exchange between the off-chip and on-chip memory, and reduce the spike latency and energy consumption by up to 17% and 85%, respectively, compared with the state-of-the-art.KeywordsNeuromorphic processorSpiking convolutional neural networksSplittingPartitioningMapping