ReSiPE: ReRAM-based single-spiking processing-in-memory engine

Processing-in-memory (PIM) designs that leverage emerging nanotechnologies like resistive random access memory (ReRAM) have demonstrated enormous potential in accelerating deep learning applications due to high energy efficiency and integration density. The common approach of existing ReRAM-based PIM designs is to encode data into either voltage levels with the assist of power-thirsty analog/digital conversion circuits or spike series by sacrificing computing latency. In this paper, we introduce ReSiPE, a ReRAM-based Single-spiking PIM Engine, which uses the arrival time of a single spike to represent the data. We analyze how to encode data into a set of single spikes and develop the circuit to realize the matrix-based computation. The proposed design can minimize the spike numbers, shorten the computation period, and thus improve energy efficiency dramatically. Our simulation results show that ReSiPE achieves 67.1% power reduction and 1.97× power efficiency improvement compared to rate-coding based ReRAM PIM designs under the comparable area, throughput, and accuracy.