  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 XNOR-based 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 bit-cell (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 micro-amps. Our results show that the proposed XNOR-VSH array
achieves 4.8% ~ 9.0% and 37% ~ 63% lower IMC latency and energy, respectively,
with 4 % ~ 64% smaller area compared to spin-transfer-torque (STT)-MRAM and
spin-orbit-torque (SOT)-MRAM based XNOR-arrays.
