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

arXiv: https://arxiv.org/abs/2401.14428 · HTML: https://mlsanity.com/paper/2401.14428/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jan 2024.

## Built on by (most cited first, 8 of 8)

- [PIM-Opt: Demystifying Distributed Optimization Algorithms on a Real-World Processing-In-Memory System](https://mlsanity.com/paper/2404.07164/) (2024)
- [PIM-AI: A Novel Architecture for High-Efficiency LLM Inference](https://mlsanity.com/paper/2411.17309/) (2024)
- [SwiftRL: Towards Efficient Reinforcement Learning on Real Processing-In-Memory Systems](https://mlsanity.com/paper/2405.03967/) (2024)
- [AI Compute Architecture and Evolution Trends](https://mlsanity.com/paper/2508.21394/) (2025)
- [AIM: Software and Hardware Co-design for Architecture-level IR-drop Mitigation in High-performance PIM](https://mlsanity.com/paper/2511.04321/) (2025)
- [Efficient In-Memory Acceleration of Sparse Block Diagonal LLMs](https://mlsanity.com/paper/2510.11192/) (2025)
- [SafeCiM: Investigating Resilience of Hybrid Floating-Point Compute-in-Memory Deep Learning Accelerators](https://mlsanity.com/paper/2512.00059/) (2025)
- [ThRIve: Thermally Robust CNN Inference via Low-Rank Adaptation in Heterogeneous PIM Architectures](https://mlsanity.com/paper/2607.17091/) (2026)

## Abstract (arXiv)

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.