A Hybrid Digital-Analog Compute-in-Memory Using Content-Addressable Memory With Flexible Multi-Bit Slicing

Abstract

Compute-in-memory (CIM) reduces data movement and enhances compute parallelism, making it suitable for AI applications. However, analog CIMs, yet energy-efficient, are vulnerable to PVT variations, while digital CIMs offer robustness but limited efficiency due to their bit-wise computation overhead. To address these challenges, we propose a hybrid CIM architecture that integrates content-addressable memory (CAM) and cluster-based CIM, named CAM-CIM, fabricated in 65nm CMOS technology. The proposed CAM-CIM flexibly slices multi-bit weights, assigning MSBs to CAM and LSBs to CIM, enabling dynamic accuracy-efficiency trade-offs across various bit precisions. A two-stage 8:3 compressor-based adder tree improves CAM efficiency and a reference voltage search algorithm ensures accurate CIM computation with low-bit ADCs. Our CAM-CIM supports 1-8b inputs/weights with reconfigurable compute modes, leveraging ternary-CAM based selective columns and cluster-wise CIM processing to produce multiple trade-off points even in the same bit precision. A prototype chip with a RISC-V controller and custom instructions is demonstrated that shows energy efficiencies of 32.4TOPS/W (8b/8b) and 76.0-354.9TOPS/W (4b/4b) with 0.66% accuracy loss, on average, across a wide range of DNN benchmarks including CNNs and vision transformers on CIFAR and ImageNet datasets.