HSC: A Hybrid Spin/CMOS Logic Based In-Memory Engine with Area-Efficient Mapping Strategy

Abstract

Recent advances in deep learning have shown that binary neural networks (BNNs) can provide a satisfying accuracy on various tasks with significant reduction in computation power and memory cost. Theoretically, the multiply-and-accumulate (MAC) operations of BNNs can be replaced by in-memory XNOR operations, thereby avoiding frequent data transfer between the buffer and the processor. However, devices supporting in-memory implementation of XNOR operations together with efficient weight-matrix mapping strategy is still an open research area. In this paper, a hybrid spin/CMOS cell (HSC) structure is proposed in which the XNOR operation can be simply realized in an in-memory computing manner by the non-volatile data from the spin component and the volatile data from the CMOS component. Given the time/spatial trade-off, a novel weight mapping method to break the large memory array and unroll the 3D kernel into 2D weight matrix is designed to cooperate with the proposed HSC structure in a time-division way. System-level simulation results show that the proposed BNN processor can achieve a 3.32* speedup and 11.9* improvement in throughput and energy efficiency, which could be attributed to the device and mapping method co-design.