C-CIM: A Multi-Mode <u>C</u> onvolution-Capable SRAM- <u>CIM</u>

SRAM is widely used in computing-in-memory (CIM) neural network accelerators because of its relatively mature technology and good compatibility with complementary metal oxide semiconductor logic process. Digital SRAM-CIM is favored by researchers because of its stability and accuracy. However, the current digital SRAM-CIM macro only supports the weight-stationary dataflows, which means the repeated movement of graph data. Some special deep neural network layers, such as depth-wise, make the utilization of computing resources inside CIM low. To overcome these problems, we propose C-CIM, which can switch between input-stationary and weight-stationary dataflows and support matrix multiplication as well as convolution operations with multiple mainstream convolution kernel sizes (1×1, 3×3, 5×5 and 7×7). The C-CIM achieves an average performance of 27.31TOPS/W@8b at a frequency of 1GHz. Experimental results show that our proposed SRAM-CIM successfully outperforms baseline in terms of performance optimization, achieving up to 7.6× performance speedup and up to 86.84% reduction in activation relocation.