An Approximate Digital CIM Macro With Low-Power Multiply-Add Units and Dynamic Sparse-Adaptive Configuring for Edge AI Inference

This paper presents an approximate digital compute-in-memory (CIM) macro for low-power edge AI inference. It introduces three hierarchical innovations: 1) novel fused approximate multiply-add units (FAMUs) that reduces power and area consumption; 2) a bit-critical weight allocation architecture that optimally balances accuracy and hardware cost; and 3) a dynamic sparsity-adaptive configuration method to minimize accuracy loss in real-time. The macro achieves an energy efficiency of 60.35 TOPS/W and an area efficiency of 1105 GOPS/mm for INT8 MACs, outperforming prior works. It attains negligible accuracy degradation on multiple mainstream datasets and suits well for edge AI inference.