PIMCOMP: A Universal Compilation Framework for Crossbar-based PIM DNN Accelerators
Xiaotian Sun, Xinyu Wang, Wanqian Li, Lei Wang, Yinhe Han, Xiaoming Chen
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 1.6× and 2.4× in throughput and latency, respectively, relative to PUMA.
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引用它的顶会 Paper2
- CIMFlow: An Integrated Framework for Systematic Design and Evaluation of Digital CIM ArchitecturesYingjie Qi, Jianlei Yang, Yiou Wang, Yikun Wang 等DAC 2025 · 被引用 2 次
- Be CIM or Be Memory: A Dual-mode-aware DNN Compiler for CIM AcceleratorsShixin Zhao, Yuming Li, Bing Li, Yintao He 等ASPLOS 2025 · 被引用 2 次
它引用的顶会 Paper2
- BRAHMS: Beyond Conventional RRAM-based Neural Network Accelerators Using Hybrid Analog Memory SystemTao Song, Xiaoming Chen, Xiaoyu Zhang, Yinhe HanDAC 2021 · 被引用 15 次
- InfoX: an energy-efficient ReRAM accelerator design with information-lossless low-bit ADCsYintao He, Songyun Qu, Ying Wang, Bing Li 等DAC 2022 · 被引用 10 次
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