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This paper presents a tutorial and review of SRAM-based Compute-in-Memory (CIM) circuits, with a focus on both Digital CIM (DCIM) and Analog CIM (ACIM) implementations. We explore the fundamental concepts, architectures, and operational principles of
Externí odkaz:
http://arxiv.org/abs/2411.06079
Autor:
Zhang, Wenlun, Ando, Shimpei, Chen, Yung-Chin, Miyagi, Satomi, Takamaeda-Yamazaki, Shinya, Yoshioka, Kentaro
Publikováno v:
IEEE/ACM International Conference on Computer-Aided Design (ICCAD 2024)
Approximate computing emerges as a promising approach to enhance the efficiency of compute-in-memory (CiM) systems in deep neural network processing. However, traditional approximate techniques often significantly trade off accuracy for power efficie
Externí odkaz:
http://arxiv.org/abs/2408.16246
Autor:
Chen, Yung-Chin, Ando, Shimpei, Fujiki, Daichi, Takamaeda-Yamazaki, Shinya, Yoshioka, Kentaro
To address the 'memory wall' problem in NN hardware acceleration, we introduce HALO-CAT, a software-hardware co-design optimized for Hidden Neural Network (HNN) processing. HALO-CAT integrates Layer-Penetrative Tiling (LPT) for algorithmic efficiency
Externí odkaz:
http://arxiv.org/abs/2312.06086
Autor:
Chen, Yung-Chin, Ando, Shimpei, Fujiki, Daichi, Takamaeda-Yamazaki, Shinya, Yoshioka, Kentaro
Computing-in-Memory (CIM) has shown great potential for enhancing efficiency and performance for deep neural networks (DNNs). However, the lack of flexibility in CIM leads to an unnecessary expenditure of computational resources on less critical oper
Externí odkaz:
http://arxiv.org/abs/2308.15040
Akademický článek
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