PIMCOMP: A Universal Compilation Framework for Crossbar-based PIM DNN Accelerators

被引:0
|
作者
Sun, Xiaotian [1 ]
Wang, Xinyu [1 ]
Li, Wanqian [1 ]
Wang, Lei [1 ]
Han, Yinhe [1 ]
Chen, Xiaoming [1 ]
机构
[1] Univ Chinese Acad Sci, Chinese Acad Sci, Inst Comp Technol, Ctr Intelligent Comp Syst, Beijing, Peoples R China
基金
国家重点研发计划; 中国国家自然科学基金;
关键词
NVM; PIM accelerator; deep neural network; compilation framework;
D O I
10.1109/DAC56929.2023.10247928
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Crossbar-based PIM DNN accelerators can provide massively parallel in-situ operations. A specifically designed compiler is important to achieve high performance for a wide variety of DNN workloads. However, some key compilation issues such as parallelism considerations, weight replication selection, and array mapping methods have not been solved. In this work, we propose PIMCOMP - a universal compilation framework for NVM crossbar-based PIM DNN accelerators. PIMCOMP is built on an abstract PIM accelerator architecture, which is compatible with the widely used Crossbar/IMA/Tile/Chip hierarchy. On this basis, we propose four general compilation stages for crossbar-based PIM accelerators: node partitioning, weight replicating, core mapping, and dataflow scheduling. We design two compilation modes with different inter-layer pipeline granularities to support high-throughput and low-latency application scenarios, respectively. Our experimental results show that PIMCMOP yields improvements of 1.6x and 2.4x in throughput and latency, respectively, relative to PUMA.
引用
收藏
页数:6
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