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The Landscape of Compute-near-memory and Compute-in-memory: A Research and Commercial Overview

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arxiv 2401.14428 v1 pith:SOM6YJJI submitted 2024-01-24 cs.AR

Asif Ali Khan , João Paulo C. De Lima , Hamid Farzaneh , Jeronimo Castrillon This is my paper · ORCID
classification cs.AR
keywords computingarchitecturesdataenergylandscapeparadigmstermsacademia
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In today's data-centric world, where data fuels numerous application domains, with machine learning at the forefront, handling the enormous volume of data efficiently in terms of time and energy presents a formidable challenge. Conventional computing systems and accelerators are continually being pushed to their limits to stay competitive. In this context, computing near-memory (CNM) and computing-in-memory (CIM) have emerged as potentially game-changing paradigms. This survey introduces the basics of CNM and CIM architectures, including their underlying technologies and working principles. We focus particularly on CIM and CNM architectures that have either been prototyped or commercialized. While surveying the evolving CIM and CNM landscape in academia and industry, we discuss the potential benefits in terms of performance, energy, and cost, along with the challenges associated with these cutting-edge computing paradigms.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. On Design Principles for Efficient Heterogeneous DRAM-PIM-GPU Systems

    cs.AR 2026-08 conditional novelty 6.0 of 10

    Simulating DRAM-PIM-GPU systems for LLM decode shows static power dominates efficiency accounting, channel scaling plateaus, and workload mapping gives bounded gains.

  2. Harnessing the Full Potential of RRAMs through Scalable and Distributed In-Memory Computing with Integrated Error Correction

    cs.DC 2025-08 conditional novelty 4.0 of 10

    A simulation framework using triple-product error cancellation plus Tikhonov denoising claims to make low-precision RRAMs match high-precision ones on matrix-vector multiplication.

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