SUN: Dynamic Hybrid-Precision SRAM-Based CIM Accelerator With High Macro Utilization Using Structured Pruning Mixed-Precision Networks

• Published in: IEEE transactions on computer-aided design of integrated circuits and systems Vol. 43; no. 7; pp. 2163 - 2176
• Main Authors: Chen, Yen-Wen, Wang, Rui-Hsuan, Cheng, Yu-Hsiang, Lu, Chih-Cheng, Chang, Meng-Fan, Tang, Kea-Tiong
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.07.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive filters; Algorithms; Artificial intelligence; Artificial neural networks; Co-design; Common Information Model (computing); Compression algorithms; Computational modeling; Computer architecture; computing-in-memory (CIM); Convolutional neural networks; deep learning; Efficiency; Hardware; Machine learning; Memory management; quantization; Quantization (signal); Random access memory; Static random access memory; Utilization

Convolutional neural networks (CNNs) play a key role in many deep learning applications; however, these networks are resource intensive. The parallel computing ability of computing-in-memory (CIM) enables high energy efficiency in artificial intelligence accelerators. When implementing a CNN in CIM,...