General Matrix Multiply (GEMM) hardware, employing large arrays of multiply-accumulate (MAC) units, perform bulk of the computation in deep learning (DL). Recent trends have established 8-bit integer (INT8) as the most widely used precision for DL inference. This paper proposes a novel MAC design capable of dynamically exploiting bit sparsity (i.e., number of `0' bits within a binary value) in input data to achieve significant improvements on area, power and energy. The proposed architecture, called OzMAC (Omit-zero-MAC), skips over zeros within a binary input value and performs simple shift-and-add-based compute in place of expensive multipliers. We implement OzMAC in SystemVerilog and present post-synthesis performance-power-area (PPA) results using commercial TSMC N5 (5nm) process node. Using eight pretrained INT8 deep neural networks (DNNs) as benchmarks, we demonstrate the existence of high bit sparsity in real DNN workloads and show that 8-bit OzMAC improves all three metrics of area, power, and energy significantly by 21%, 70%, and 28%, respectively. Similar improvements are achieved when scaling data precisions (4, 8, 16 bits) and clock frequencies (0.5 GHz, 1 GHz, 1.5 GHz). For the 8-bit OzMAC, scaling its frequency to normalize the throughput relative to conventional MAC, it still achieves 30% improvement on both power and energy.

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