XNOR-VSH: A Valley-Spin Hall Effect-Based Compact and Energy-Efficient Synaptic Crossbar Array for Binary Neural Networks

Abstract

Binary neural networks (BNNs) have shown an immense promise for resource-constrained edge artificial intelligence (AI) platforms as their binarized weights and inputs can significantly reduce the compute, storage and communication costs. Several works have explored XNORbased BNNs using SRAMs and nonvolatile memories (NVMs). However, these designs typically need two bit-cells to encode signed weights leading to an area overhead. In this paper, we address this issue by proposing a compact and low power in-memory computing (IMC) of XNOR-based dot products featuring signed weight encoding in a single bit-cell. Our approach utilizes valley-spin Hall (VSH) effect in monolayer tungsten di-selenide to design an XNOR bitcell (named 'XNOR-VSH') with differential storage and access-transistor-less topology. We co-optimize the proposed VSH device and a memory array to enable robust in-memory dot product computations between signed binary inputs and signed binary weights with sense margin (SM) > 1 µA. Our results show that the proposed XNOR-VSH array achieves 4.8% ~ 9.0% and 37% ~ 63% lower IMC latency and energy, respectively, with 49% ~ 64% smaller area compared to spin-transfer-torque (STT)-MRAM and spin-orbit-torque (SOT)-MRAM based XNOR-arrays. We also present the impact of hardware non-idealities and process-variations in XNOR-VSH on system-level accuracy for the trained ResNet-18 BNNs using the CIFAR-10 dataset.