Abstract
SRAM-based computing in memory (SRAM-CIM) supports weight-stationary dataflows and in-situ computation, successfully achieving lower weight SRAM traffic and hence better operation intensity (OI) than Von Neumann architecture. However, sticking to weight-stationary dataflows is sub-optimal since some layers of CNNs, e.g., depth-wise and input-dominant, suffer from large activation traffic and underutilization.We propose Morphable CIM to address the above challenges, in which the key contributions are: 1) We propose a dataflow-morphable SRAM-CIM architecture that adaptively switches between weight-stationary and input-stationary dataflow to enhance the overall operation intensity (OI) and computation utilization. 2) We propose an input-stationary systolic dataflow and a word-wise mapping to efficiently achieve dataflow reconfigurability for SRAM-CIM. 3) We propose a depthwise-capable CIM macro to improve the utilization of processing depth-wise layers.The experimental results on ten workloads show that our proposed SRAM-CIM architecture successfully outperforms the traffic-optimized and the performance-optimized baselines by up to 16.7× performance speedup, 3.6× higher energy efficiency, and 94.8% memory traffic reduction.