Column-wise Quantization of Weights and Partial Sums for Accurate and Efficient Compute-In-Memory Accelerators

• URL: http://arxiv.org/abs/2502.07842v1
• Date: Tue, 11 Feb 2025 05:32:14 GMT
• Title: Column-wise Quantization of Weights and Partial Sums for Accurate and Efficient Compute-In-Memory Accelerators
• Authors: Jiyoon Kim, Kang Eun Jeon, Yulhwa Kim, Jong Hwan Ko,
• Abstract summary: CIM is an efficient method for implementing deep neural networks (DNNs) CIM suffers from substantial overhead from analog-to-digital converters (ADCs) Low-bit weight constraints, im- posed by cell limitations and the need for multiple cells present further challenges. This work addresses these challenges by aligning weight and partial-sum quantization granularities at the column-wise level.
• Score: 7.728820930581886
• License:
• Abstract: Compute-in-memory (CIM) is an efficient method for implementing deep neural networks (DNNs) but suffers from substantial overhead from analog-to-digital converters (ADCs), especially as ADC precision increases. Low-precision ADCs can re- duce this overhead but introduce partial-sum quantization errors degrading accuracy. Additionally, low-bit weight constraints, im- posed by cell limitations and the need for multiple cells for higher- bit weights, present further challenges. While fine-grained partial- sum quantization has been studied to lower ADC resolution effectively, weight granularity, which limits overall partial-sum quantized accuracy, remains underexplored. This work addresses these challenges by aligning weight and partial-sum quantization granularities at the column-wise level. Our method improves accuracy while maintaining dequantization overhead, simplifies training by removing two-stage processes, and ensures robustness to memory cell variations via independent column-wise scale factors. We also propose an open-source CIM-oriented convolution framework to handle fine-grained weights and partial-sums effi- ciently, incorporating a novel tiling method and group convolution. Experimental results on ResNet-20 (CIFAR-10, CIFAR-100) and ResNet-18 (ImageNet) show accuracy improvements of 0.99%, 2.69%, and 1.01%, respectively, compared to the best-performing related works. Additionally, variation analysis reveals the robust- ness of our method against memory cell variations. These findings highlight the effectiveness of our quantization scheme in enhancing accuracy and robustness while maintaining hardware efficiency in CIM-based DNN implementations. Our code is available at https://github.com/jiyoonkm/ColumnQuant.

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