SIMSnn: A Weight-Agnostic ReRAM-based Search-In-Memory Engine for SNN Acceleration

Abstract

Bio-plausible spiking neural networks (SNNs) have gained a great momentum due to its inherent efficiency of processing event-driven information. The dominant computation-matrix bit-wise And-Add operations-in SNN is naturally fit for process-in-memory architecture (PIM). The long input spike train of SNN and the bit-serial processing mechanism of PIM, however, incur considerable latency and frequent analog-to-digital conversion, offsetting the performance gain and energy-efficiency. In this paper, we propose a novel Search-in-Memory (SIM) architecture to accelerate the SNN inference, named SIMSnn. Rather than processing the input bit-by-bit over multiple time steps, SIMSnn can take in a sequence of spikes and search the result by parallel associative matches in the CAM crossbar. As a weight-agnostic SNN accelerator, SIMSnn can adapt to various evolving SNNs without rewriting the crossbar array.