An LSGQ-FFS Framework for Adaptive Optimization of Hybrid INT-CIM Architecture
An LSGQ-FFS Framework for Adaptive Optimization of Hybrid INT-CIM Architecture
Abstract:
Hybrid computing-in-memory (CIM) has recently gained significant attention due to its ability to leverage the strengths of both digital CIM (DCIM) and analog CIM (ACIM). The multibit fusion (MF) scheme enhances energy efficiency by fusing low-bit results, which typically require multiple read-out cycles, into a single-cycle read out. However, the relationship between hybrid INT-CIM circuit design and network performance based on the MF scheme has not yet been systematically explored. In addition, we investigate how different MF configurations affect the performance of various neural networks. To address this gap, we first propose a less-significant group quantization (LSGQ) model, which defines and explores the design space of hybrid INT-CIM. Second, we develop a FastFuse-Search (FFS) algorithm, which optimizes configurations for different networks to strike a better balance between model accuracy and energy efficiency. Based on the experimental results, some key considerations on hybrid CIM design are derived. FFS yields a 1.72× energy-efficiency boost with negligible accuracy loss. Finally, we fabricate a 28-nm hybrid INT-CIM test chip, achieving 59.74 TOPS/W and 0.96 TOPS/mm2 , with performance metrics of 23.21 perplexity for GPT-2, 68.69% accuracy for ResNet18, and 80.53% accuracy for ViT.
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An LSGQ-FFS Framework for Adaptive Optimization of Hybrid INT-CIM Architecture
