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Compute-in-memory (CIM) is a promising approach for realizing energy-efficient convolutional neural network (CNN) accelerators. Previous CIM works demonstrated a high peak energy efficiency of over 100 TOPS/W, with larger fabrics of 1000 + channels. Yet, they typically suffer from low utilization for small CNN layers (e. g. , 9% for ResNet-32). It penalizes their average energy efficiency, throughput density, and effective memory size by the utilization rate. In addition, the analog-to-digital converter (ADC) occupies most of their computing time (90%), further hindering the CIM's throughput. This work presents an FLEX-CIM fabricated under 28-nm CMOS featuring: 1) an analog partial sum (APS) circuit to enable a flexible CIM Kernel size; 2) an overclocked fast multiply–accumulate array (FMA) to boost the throughput; and 3) an adaptive-resolution ADC to enhance the throughput and energy efficiency. The achieved utilization is 99. 2% on ResNet-32. Under 4-bit MAC precision, the peak energy efficiency is 181. 6 TOPS/W, and the peak throughput density is 25. 63 TOPS/mm ^2.
Fu et al. (Mon,) studied this question.