# A Modern Primer on Processing in Memory arXiv: https://arxiv.org/abs/2012.03112 ยท HTML: https://mlsanity.com/paper/2012.03112/ ## Key facts - Cited by 32 papers in the map (data through 14 Oct 2026). - First posted on arXiv 5 Dec 2020. ## Built on by (most cited first, 30 of 32) - [Demystifying Chains, Trees, and Graphs of Thoughts](https://mlsanity.com/paper/2401.14295/) (2024) - [Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysis](https://mlsanity.com/paper/2205.09702/) (2022) - [PAPI: Exploiting Dynamic Parallelism in Large Language Model Decoding with a Processing-In-Memory-Enabled Computing System](https://mlsanity.com/paper/2502.15470/) (2025) - [Design Principles for Lifelong Learning AI Accelerators](https://mlsanity.com/paper/2310.04467/) (2023) - [Accelerating Neural Network Inference with Processing-in-DRAM: From the Edge to the Cloud](https://mlsanity.com/paper/2209.08938/) (2022) - [Hyperdimensional Representation Learning for Node Classification and Link Prediction](https://mlsanity.com/paper/2402.17073/) (2024) - [PIM-Opt: Demystifying Distributed Optimization Algorithms on a Real-World Processing-In-Memory System](https://mlsanity.com/paper/2404.07164/) (2024) - [PIMphony: Overcoming Bandwidth and Capacity Inefficiency in PIM-based Long-Context LLM Inference System](https://mlsanity.com/paper/2412.20166/) (2024) - [Machine Learning Training on a Real Processing-in-Memory System](https://mlsanity.com/paper/2206.06022/) (2022) - [Demystifying Higher-Order Graph Neural Networks](https://mlsanity.com/paper/2406.12841/) (2024) - [Open3DBench: Open-Source Benchmark for 3D-IC Backend Implementation and PPA Evaluation](https://mlsanity.com/paper/2503.12946/) (2025) - [PyGim: An Efficient Graph Neural Network Library for Real Processing-In-Memory Architectures](https://mlsanity.com/paper/2402.16731/) (2024) - [CHIME: Chiplet-based Heterogeneous Near-Memory Acceleration for Edge Multimodal LLM Inference](https://mlsanity.com/paper/2601.19908/) (2025) - [An Experimental Evaluation of Machine Learning Training on a Real Processing-in-Memory System](https://mlsanity.com/paper/2207.07886/) (2022) - [SwiftRL: Towards Efficient Reinforcement Learning on Real Processing-In-Memory Systems](https://mlsanity.com/paper/2405.03967/) (2024) - [EcoFlow: Efficient Convolutional Dataflows for Low-Power Neural Network Accelerators](https://mlsanity.com/paper/2202.02310/) (2022) - [Heterogeneous Data-Centric Architectures for Modern Data-Intensive Applications: Case Studies in Machine Learning and Databases](https://mlsanity.com/paper/2205.14664/) (2022) - [TransPimLib: A Library for Efficient Transcendental Functions on Processing-in-Memory Systems](https://mlsanity.com/paper/2304.01951/) (2023) - [Comprehensive Benchmarking of Binary Neural Networks on NVM Crossbar Architectures](https://mlsanity.com/paper/2308.06227/) (2023) - [WWW: What, When, Where to Compute-in-Memory](https://mlsanity.com/paper/2312.15896/) (2023) - [Assessing Tenstorrent's RISC-V MatMul Acceleration Capabilities](https://mlsanity.com/paper/2505.06085/) (2025) - [AIM: Software and Hardware Co-design for Architecture-level IR-drop Mitigation in High-performance PIM](https://mlsanity.com/paper/2511.04321/) (2025) - [Physical Foundation Models: Fixed hardware implementations of large-scale neural networks](https://mlsanity.com/paper/2604.27911/) (2026) - [NicePIM: Design Space Exploration for Processing-In-Memory DNN Accelerators with 3D-Stacked-DRAM](https://mlsanity.com/paper/2305.19041/) (2023) - [Prospective Messaging: Learning in Networks with Communication Delays](https://mlsanity.com/paper/2407.05494/) (2024) - [A Data Aggregation Visualization System supported by Processing-in-Memory](https://mlsanity.com/paper/2503.08463/) (2025) - [DCC: Data-Centric Compilation of Machine Learning Kernels for Processing-In-Memory Architectures](https://mlsanity.com/paper/2511.15503/) (2025) - [Machine Learning-Driven Intelligent Memory System Design: From On-Chip Caches to Storage](https://mlsanity.com/paper/2603.14583/) (2026) - [NasZip: Software and Hardware Co-Design to Accelerate Approximate Nearest Neighbor Search with DIMM-Based Near-Data Processing](https://mlsanity.com/paper/2605.21952/) (2026) - [Leveraging ECRAM for Edge Continual Learning](https://mlsanity.com/paper/2607.19661/) (2026) ## Abstract (arXiv) This paper discusses recent research that aims to enable computation close to data, an approach we broadly call processing-in-memory (PIM). PIM places computation mechanisms in or near where the data is stored (i.e., inside memory chips or modules, in the logic layer of 3D-stacked memory, in the memory controllers, in storage devices or chips), so that data movement between the computation units and memory/storage units is reduced or eliminated. While the general idea of PIM is not new, we discuss motivating trends in applications as well as memory circuits and technology that greatly exacerbate the need for enabling it in modern computing systems. We examine at least two promising new approaches to designing PIM systems to accelerate important data-intensive applications: (1) processing-using-memory, which exploits fundamental analog operational principles of memory chips to perform massively-parallel operations in-situ in memory, (2) processing-near-memory, which exploits different logic and memory integration technologies (e.g., 3D-stacked memory technology) to place computation logic close to memory circuitry, and thereby enable high-bandwidth, low-energy, and low-latency access to data. In both approaches, we describe and tackle relevant cross-layer research, design, and adoption challenges in devices, architecture, systems, compilers, programming models, and applications. Our focus is on the development of PIM designs that can be adopted in real computing platforms at low cost. We conclude by discussing work on solving key challenges to the practical adoption of PIM. We believe that the shift from a processor-centric to a memory-centric mindset (and infrastructure) remains the largest adoption challenge for PIM, which, once overcome, can unleash a fundamentally energy-efficient, high-performance, and sustainable new way of designing, using, and programming computing systems.