Be CIM or Be Memory: A Dual-mode-aware DNN Compiler for CIM Accelerators
Shixin Zhao, Yuming Li, Bing Li, Yintao He, Mengdi Wang, Yinhe Han,, Ying Wang

TL;DR
This paper introduces CMSwitch, a compiler that optimizes resource allocation for CIM accelerators with dynamic mode-switching, significantly improving DNN application performance by leveraging compute-memory flexibility.
Contribution
The paper presents CMSwitch, a novel compiler that integrates adaptive mode-switching into CIM compilation, enabling better resource management and performance for diverse DNN workloads.
Findings
Achieves 1.31× average speedup over state-of-the-art CIM compilation methods.
Effectively exploits CIM's compute-memory mode-switching for real-world DNNs.
Demonstrates improved performance across various DNN applications.
Abstract
Computing-in-memory (CIM) architectures demonstrate superior performance over traditional architectures. To unleash the potential of CIM accelerators, many compilation methods have been proposed, focusing on application scheduling optimization specific to CIM. However, existing compilation methods often overlook CIM's capability to switch dynamically between compute and memory modes, which is crucial for accommodating the diverse memory and computational needs of real-world deep neural network architectures, especially the emerging large language models. To fill this gap, we introduce CMSwitch, a novel compiler to optimize resource allocation for CIM accelerators with adaptive mode-switching capabilities, thereby enhancing the performance of DNN applications. Specifically, our approach integrates the compute-memory mode switch into the CIM compilation optimization space by introducing a…
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Taxonomy
TopicsPower Systems and Technologies · Advanced Data Storage Technologies · Distributed and Parallel Computing Systems
