To PIM or not for emerging general purpose processing in DDR memory systems
Alexandar Devic, Siddhartha Balakrishna Rai, Anand Sivasubramaniam, Ameen Akel, Sean Eilert, Justin Eno
Abstract
As Processing-In-Memory (PIM) hardware matures and starts making its way into normal compute platforms, software has an important role to play in determining what to perform where, and when, on such heterogeneous systems. Taking an emerging class of PIM hardware which provisions a general purpose (RISC-V) processor at each memory bank, this paper takes on this challenging problem by developing a software compilation framework. This framework analyzes several application characteristics - parallelizability, vectorizability, data set sizes, and offload costs - to determine what, whether, when and how to offload computations to the PIM engines. In the process, it also proposes a vector engine extension to the bank-level RISC-V cores. Using several off-the-shelf C/C++ applications, we demonstrate that PIM is not always a panacea, and a framework such as ours is essential in carefully selecting what needs to be performed where, when and how. The choice of hardware platforms - number of memory banks, relative speeds and capabilities of host CPU and PIM cores, can further impact the "to PIM or not" question.
Ask about this paper
Ask your agent about it.
Lune has read the top-tier papers around this one, so every answer names the papers it rests on.
Your agent calls
Lunesearch_papers
Free to start. No credit card required.
Terminal
Install the CLIlune papers get 7f6e2e73-35b4-4976-93ff-0ae73ad1e5e7Cited by top-tier papers11
- Pathfinding Future PIM Architectures by Demystifying a Commercial PIM TechnologyBongjoon Hyun, Taehun Kim, Dongjae Lee, Minsoo RhuHPCA 2024 · 62 citations
- PIM Is All You Need: A CXL-Enabled GPU-Free System for Large Language Model InferenceYufeng Gu, Alireza Khadem, Sumanth Umesh, Ning Liang et al.ASPLOS 2025 · 44 citations
- PIM-MMU: A Memory Management Unit for Accelerating Data Transfers in Commercial PIM SystemsDongjae Lee, Bongjoon Hyun, Taehun Kim, Minsoo RhuMICRO 2024 · 23 citations
- Instant-NeRF: Instant On-Device Neural Radiance Field Training via Algorithm-Accelerator Co-Designed Near-Memory ProcessingYang Katie Zhao, Shang Wu, Jingqun Zhang, Sixu Li et al.DAC 2023 · 18 citations
- UniNDP: A Unified Compilation and Simulation Tool for Near DRAM Processing ArchitecturesTongxin Xie, Zhenhua Zhu, Bing Li, Yukai He et al.HPCA 2025 · 9 citations
Related papers
- OptiPIM: Optimizing Processing-in-Memory Acceleration Using Integer Linear ProgrammingJiantao Liu, Minxuan Zhou, Yue Pan, Chien-Yi Yang et al.ISCA 2025 · 6 citations
- AttenPIM: Accelerating LLM Attention with Dual-mode GEMV in Processing-in-MemoryLiyan Chen, Dongxu Lyu, Zhenyu Li, Jianfei Jiang et al.DAC 2025 · 2 citations
- A Case Study of Processing-in-Memory in off-the-Shelf SystemsJoel Nider, Craig Mustard, Andrada Zoltan, John Ramsden et al.USENIX ATC 2021 · 62 citations
- Accelerating Transactional Execution via Processing-In-MemoryAndré Lopes, Daniel Castro, Paolo RomanoEuroSys 2026
- CAPE: A Content-Addressable Processing EngineHelena Caminal, Kailin Yang, Srivatsa Srinivasa, Akshay Krishna Ramanathan et al.HPCA 2021 · 30 citations
