Efficient SRAM-PIM Architecture Design by Exploiting Unstructured Bit-Level Sparsity in Neural Networks

Bit-level sparsity in neural network models can significantly boost computational efficiency, but traditional digital SRAM-PIM architectures struggle to effectively exploit this unstructured sparsity. The proposed Dyadic Block PIM (DB-PIM) framework, through an algorithm-architecture co-design approach, efficiently utilizes unstructured bit-level sparsity to achieve remarkable speedups and energy savings.