AIM: Software and Hardware Co-design for Architecture-level IR-drop Mitigation in High-performance PIM
Yuanpeng Zhang, Xing Hu, Xi Chen, Zhihang Yuan, Cong Li, Jingchen Zhu, Zhao Wang, Chenguang Zhang, Xin Si, Wei Gao, Qiang Wu, Runsheng Wang, Guangyu Sun
Abstract
SRAM Processing-in-Memory (PIM) has emerged as the most promising implementation for high-performance PIM, delivering superior computing density, energy efficiency, and computational precision. However, the pursuit of higher performance necessitates more complex circuit designs and increased operating frequencies, which exacerbate IR-drop issues. Severe IR-drop can significantly degrade chip performance and even threaten reliability. Conventional circuit-level IR-drop mitigation methods, such as back-end optimizations, are resource-intensive and often compromise power, performance, and area (PPA). To address these challenges, we propose AIM, comprehensive software and hardware co-design for architecture-level IR-drop mitigation in high-performance PIM. Initially, leveraging the bit-serial and in-situ dataflow processing properties of PIM, we introduce R tog and HR, which establish a direct correlation between PIM workloads and IR-drop. Building on this foundation, we propose LHR and WDS, enabling extensive exploration of architecture-level IR-drop mitigation while maintaining computational accuracy through software optimization. Subsequently, we develop IR-Booster, a dynamic adjustment mechanism that integrates software-level HR information with hardwarebased IR-drop monitoring to adapt the V-f pairs of the PIM macro, achieving enhanced energy efficiency and performance. Finally, we propose the HR-aware task mapping method, bridging software and hardware designs to achieve optimal improvement. Post-layout simulation results on a 7nm 256-TOPS PIM chip demonstrate that AIM achieves up to 69.2% IR-drop mitigation, resulting in 2.29× energy efficiency improvement and 1.152× speedup.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext da8499b8-1178-4a2a-8705-1fd82df87321Builds on9
- An Image is Worth 16x16 Words: Transformers for Image Recognition at ScaleAlexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn et al.ICLR 2021 · 21,477 citations
- Up or Down? Adaptive Rounding for Post-Training QuantizationMarkus Nagel, Rana Ali Amjad, Mart van Baalen, Christos Louizos et al.ICML 2020 · 816 citations
- BRECQ: Pushing the Limit of Post-Training Quantization by Block ReconstructionYuhang Li, Ruihao Gong, Xu Tan, Yang Yang et al.ICLR 2021 · 619 citations
- OmniQuant: Omnidirectionally Calibrated Quantization for Large Language ModelsWenqi Shao, Mengzhao Chen, Zhaoyang Zhang, Peng Xu et al.ICLR 2024 · 395 citations
- CircuitNet 2.0: An Advanced Dataset for Promoting Machine Learning Innovations in Realistic Chip Design EnvironmentXun Jiang, Zhuomin Chai, Yuxiang Zhao, Yibo Lin et al.ICLR 2024 · 32 citations
Related papers
- PIM-MMU: A Memory Management Unit for Accelerating Data Transfers in Commercial PIM SystemsDongjae Lee, Bongjoon Hyun, Taehun Kim, Minsoo RhuMICRO 2024 · 23 citations
- CP-SRAM: charge-pulsation SRAM marco for ultra-high energy-efficiency computing-in-memoryHe Zhang, Linjun Jiang, Jianxin Wu, Tingran Chen et al.DAC 2022 · 12 citations
- OptiPIM: Optimizing Processing-in-Memory Acceleration Using Integer Linear ProgrammingJiantao Liu, Minxuan Zhou, Yue Pan, Chien-Yi Yang et al.ISCA 2025 · 6 citations
- PIPF-DRAM: processing in precharge-free DRAMNezam Rohbani, Mohammad Arman Soleimani, Hamid Sarbazi-AzadDAC 2022 · 7 citations
- Towards Efficient SRAM-PIM Architecture Design by Exploiting Unstructured Bit-Level SparsityCenlin Duan, Jianlei Yang, Yiou Wang, Yikun Wang et al.DAC 2024 · 6 citations
