Modeling of Less-Significant Group Quantization for Hybrid CIM Architecture

Hybrid computing-in-memory (CIM) has gained growing interest in recent times due to its ability to combine the strengths of both digital CIM (DCIM) and analog CIM (ACIM). The Multi-Bit Fusion (MF) scheme enhances energy efficiency by fusing low-bit results, which would typically require multiple readout cycles, into a single-cycle readout. However, the relationship between hybrid INT-CIM circuit design and network performance based on the MF scheme has yet to be systematically explored. To fill this gap, a less-significant group quantization (LSGQ) model is proposed, defining and exploring the hybrid INT-CIM design space. Experimental results demonstrate up to 1.83x improvement in energy efficiency with minimal impact on performance. A 28nm hybrid INT-CIM test chip is fabricated, achieving 52.16 TOPS/W, 0.96 TOPS/mm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>, with respective performance metrics of 48.58 perplexity for GPT-2, 75.51% accuracy for ResNet18, and 81.5% accuracy for ViT.