WWW: What, When, Where to Compute-in-Memory

[2312.15896] WWW: What, When, Where to Compute-in-Memory Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Hardware Architecture arXiv:2312.15896 (cs) [Submitted on 26 Dec 2023 ( v1 ), last revised 27 Feb 2025 (this version, v3)] Title: WWW: What, When, Where to Compute-in-Memory Authors: Tanvi Sharma , Mustafa Ali , Indranil Chakraborty , Kaushik Roy View a PDF of the paper titled WWW: What, When, Where to Compute-in-Memory, by Tanvi Sharma and 3 other authors View PDF HTML (experimental) Abstract: Matrix multiplication is the dominant computation during Machine Learning (ML) inference. To efficiently perform such multiplication operations, Compute-in-memory (CiM) paradigms have emerged as a highly energy efficient solution. However, integrating compute in memory poses key questions, such as 1) What type of CiM to use: Given a multitude of CiM design characteristics, determining their suitability from architecture perspective is needed. 2) When to use CiM: ML inference includes workloads with a variety of memory and compute requirements, making it difficult to identify when CiM is more beneficial than standard processing cores. 3) Where to integrate CiM: Each memory level has different bandwidth and capacity, creating different data reuse opportunities for CiM integration. To answer such questions regarding on-chip CiM integration for accelerating ML workloads, we use an analytical architecture-evaluation methodology with tailored mapping algorithm. The mapping algorithm aims to achieve highest weight reuse and reduced data movements for a given CiM prototype and workload. Our analysis considers the integration of CiM prototypes into the cache levels of a tensor-core-like architecture, and shows that CiM integrated memory improves energy efficiency by up to 3.4x and throughput by up to 15.6x compared to established baseline with INT-8 precision. We believe the proposed work provides insights into what type of CiM to use, and when and where to optimally integrate it in the cache hierarchy for efficient matrix multiplication. Comments: added supplementary Subjects: Hardware Architecture (cs.AR) ; Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG) Cite as: arXiv:2312.15896 [cs.AR] (or arXiv:2312.15896v3 [cs.AR] for this version) https://doi.org/10.48550/arXiv.2312.15896 Focus to learn more arXiv-issued DOI via DataCite Journal reference: IEEE Transactions on Emerging Topics in Computing 2025.03 Related DOI : https://doi.org/10.1109/TETC.2025.3574508 Focus to learn more DOI(s) linking to related resources Submission history From: Tanvi Sharma [ view email ] [v1] Tue, 26 Dec 2023 06:16:12 UTC (4,019 KB) [v2] Thu, 20 Jun 2024 14:29:16 UTC (3,733 KB) [v3] Thu, 27 Feb 2025 21:50:48 UTC (8,010 KB) Full-text links: Access Paper: View a PDF of the paper titled WWW: What, When, Where to Compute-in-Memory, by Tanvi Sharma and 3 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.AR < prev | next > new | recent | 2023-12 Change to browse by: cs cs.DC cs.LG References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

Related documents

• On the Performance of Software Transactional Memory#320067
• Analysis of vertical AND flash memory for energy-efficient, scalable, fast CIM beyond vertical NAND flash memory.#138552
• Human Memory: A Proposed System and its Control Processes#7148
• Modelado y simulación de sistemas digitales utilizando VHDL a través del WWW#355419
• Theory of Transactional Memory#1100239