Binary neural networks (BNNs) have shown an immense promise for resource-constrained edge artificial intelligence (AI) platforms. However, prior designs typically either require two bit-cells to encode signed weights leading to an area overhead, or require complex peripheral circuitry. In this article, 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) eff…