PIMCOMP: A Universal Compilation Framework for Crossbar-based PIM DNN Accelerators
Xiaotian Sun, Xinyu Wang, Wanqian Li, Lei Wang, Yinhe Han, Xiaoming Chen
Abstract
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 1.6× and 2.4× in throughput and latency, respectively, relative to PUMA.
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Install the CLIlune papers fulltext 55a198cf-37e0-45f4-9aac-a104274defeeCited by top-tier papers2
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Builds on2
- BRAHMS: Beyond Conventional RRAM-based Neural Network Accelerators Using Hybrid Analog Memory SystemTao Song, Xiaoming Chen, Xiaoyu Zhang, Yinhe HanDAC 2021 · 15 citations
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