SATO: spiking neural network acceleration via temporal-oriented dataflow and architecture

Abstract

Event-driven spiking neural networks (SNNs) have shown great promise for being strikingly energy-efficient. SNN neurons integrate the spikes, accumulate the membrane potential, and fire output spike when the potential exceeds a threshold. Existing SNN accelerators, however, have to carry out such accumulation-comparison operation in serial. Repetitive spike generation at each time step not only increases latency as well as overall energy budget, but also incurs memory access overhead of fetching membrane potentials, both of which lessen the efficiency of SNN accelerators. Meanwhile, inherent highly sparse spikes of SNNs lead to imbalanced workloads among neurons that hurdle the utilization of processing elements (PEs). This paper proposes SATO, a temporal-parallel SNN accelerator that accumulates the membrane potential for all time steps in parallel. SATO architecture contains a novel binary adder-search tree to generate the output spike train, which decouples the chronological dependence in the accumulation-comparison operation. Moreover, SATO can evenly dispatch the compressed workloads to all PEs with maximized data locality of input spike trains based on a bucket-sort-based method. Our evaluations show that SATO outperforms the previous ANN accelerator 8-bit version of "Eyeriss" by 30.9× in terms of speedup and 12.3×, in terms of energy-saving. Compared with the state-of-the-art SNN accelerator "SpinalFlow", SATO can also achieve 6.4× performance gain and 4.8× energy reduction, which is quite impressive for inference.