Compute-in-Memory

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Overview

Compute-in-memory studies how to perform operations where data is stored, reducing the energy and time spent moving data between memory and processors. Approaches include analog matrix multiplication in resistive-memory crossbars, digital processing-in-memory, and hybrid designs, supported by workload mapping, quantization, calibration, and noise-aware training. Applications range from neural-network and large-language-model inference to edge learning, spiking networks, and probabilistic computing. A central challenge is balancing efficiency with accuracy and reliability despite device variation, limited precision, peripheral-circuit costs, and constraints on memory capacity and endurance.

Latest papers

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56 papers6 papers in the last four weeks, level with the four weeks before. 0.0% of all new papers.

  1. SLICE-CiM: Selective Layer-wise Inference Control Engine with Confidence-Based Early Exit for SRAM based Digital Compute-in-Memory

    Oct 27, 2026Pratham Sharma, Mohd Faisal Khan, Santosh Kumar VishvakarmaEfficient Neural Network InferenceEnergy-Efficient ML

    Department of Electrical Engineering, Indian Institute of Technology Indore, Indore, India

  2. 3D-MoE: Complementarity-Guided Hardware-Software Co-Design for MoE Decode on 3D-Stacked NMP

    Oct 19, 2026Yudong Pan, Lian Liu, Shixin Zhao +8Neural Network Hardware AccelerationMixture-of-Experts Inference

    SKLP, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China · University of Chinese Academy of Sciences, Beijing, China · University of Edinburgh, Edinburgh, United Kingdom

  3. ABNAT: Attention-Based Noise-Aware Training for Robust Transformers on Analog Compute-in-Memory Systems

    Oct 15, 2026Wenshuai Yao, Wenyong Zhou, Hanyong Shao +5Noise RobustnessTransformer

    School of Integrated Circuits, Peking University, Beijing, China · Department of Electrical and Computer Engineering, The University of Hong Kong, Hong Kong · School of Integrated Circuit Science and Engineering, Beihang University, Beijing, China +1

  4. Robust Parameter-Efficient LLM Adaptation on Analog Hardware

    Oct 4, 2026Jindan Li, Zhaoxian Wu, Tianyi ChenCompute-in-MemoryLLM Fine-Tuning

    Cornell University

  5. Physical Muon: Orthogonalization as an Equilibrium Computation

    Sep 29, 2026Yuren HaoPhysical Neural NetworksNeural Network Optimization

    University of Illinois at Urbana-Champaign

  6. Making Analog Training Scale: Co-Designing Mapping, Optimizer, and Converters

    Sep 29, 2026Zhaoxian Wu, Tayfun Gokmen, Omobayode Fagbohungbe +2Analog ComputingCompute-in-Memory

    Cornell Tech and Cornell University · IBM T. J. Watson Research Center · Sandia National Laboratories

  7. IMC-CLINIC: Coupled Loss-Informed Newton Iterations for Clipping in Analog In-Memory Computing

    Sep 28, 2026Yung-Chin Chen, Chia-Yu Chen, Naveen VermaLLM QuantizationCompute-in-Memory

    Princeton University, NJ, USA · EnCharge AI, CA, USA

  8. LLM Inference in a Flash!

    Sep 14, 2026Sebastian Zhao, Minseo Kim, Coleman Hooper +5LLM QuantizationCompute-in-Memory

    UC Berkeley · ICSI · LBNL

  9. A Time-Based Readout for Vector-Matrix Multiplication in Fully Analog Memristive SNNs

    Sep 10, 2026Elia Mateu-Barriendos, Álvaro Gómez-Pau, Josep Rius +3Efficient Neural Network InferenceCompute-in-Memory

    Universitat Polit`ecnica de Catalunya - BarcelonaTech (UPC)

  10. Bio-inspired Learning and Decision-Making with Probabilistic In-Memory Computing Hardware: Part 1

    Sep 10, 2026Thomas Dalgaty, Eiji Kawasaki, Miguel de Prado +2Compute-in-MemoryBayesian Inference

    CEA-List, Grenoble, France

  11. RACE-AIMC: Selective Inference for Heterogeneous Analog In-Memory Accelerators at the Edge

    Sep 2, 2026Osama Yousuf, Martin Lueker-BodenEfficient Neural Network InferenceCompute-in-Memory

    WD Research San Jose, CA, USA

  12. Compute-in-Memory Attention: A Time-Domain Analog Softmax Circuit with RC-Tunable Temperature

    Sep 2, 2026Ankur Singh, Ashish Gautam, Shruti R. Kulkarni +1Softmax AttentionAnalog Computing

    Department of Electrical and Computer Engineering, University of Wisconsin–Madison, Madison, WI, USA. · ORNL Oak Ridge, Tennessee, USA.

  13. ASSERT: Adaptive Stochastic Sampling for Robust Diffusion Models on Analog Compute-in-Memory Hardware

    Sep 1, 2026Yuannuo Feng, Yizhe Chen, Wenshuai Yao +4Compute-in-MemoryDiffusion Sampling

    School of Integrated Circuit Science and Engineering, Beihang University, Beijing, China · Zhicun Research Lab, Beijing, China · School of Integrated Circuits, Peking University, Beijing, China +1

  14. Event-triggered Implicit Perturbation for Zeroth-Order Fine-Tuning of Spiking Transformers

    Aug 21, 2026Tengteng Lei, Prabodh Katti, Rashi Dutt +5Zeroth-Order OptimizationSpiking Neural Networks

    Institute for Intelligent Networked Systems (INSI), Northeastern University London, London E1 8PH, U.K. · Centre for Intelligent Information Processing Systems (CIIPS), Department of Engineering, King’s College London, London WC2R 2LS, U.K.

  15. NANQ: Noise-Floor-Aware Mixed-Precision Non-Uniform Quantization for Analog Compute-in-Memory

    Aug 3, 2026Yizhe Chen, Wenshuai Yao, Saiya Wang +6Mixed-Precision QuantizationCompute-in-Memory

    School of Integrated Circuit Science and Engineering, Beihang University, Beijing, China · School of Integrated Circuits, Peking University, Beijing, China · Department of Electrical and Computer Engineering, The University of Hong Kong, Hong Kong SAR, China

  16. Mitigating the Impact of Retention Loss on Inference Accuracy in 65 nm Single-Poly Floating-Gate Analog In-Memory Computing

    Jul 27, 2026Mirko Brazzini, Giulio Filippeschi, Alessandro Catania +2Compute-in-Memory

    Dipartimento di Ingegneria dell’Informazione, University of Pisa, Pisa, Italy · Quantavis SRL, Pisa, Italy

  17. Multi-primitive in-memory computing for Monte Carlo tree search

    Jul 24, 2026Tergel Molom-Ochir, Benjamin F. Morris, Yintao He +6Compute-in-MemoryEnergy-Efficient ML

    Department of Electrical and Computer Engineering, Duke University, Durham, 27708, NC, USA. · Hewlett Packard Labs, Milpitas, 95035, CA, USA.

  18. RED-PIM: Reducing Data Movement for Transformers using Processing-in-Memory

    Jul 23, 2026Zahra Yousefijamarani, Alaa AlameldeenEfficient Transformer InferenceAI Accelerator Inference

    Simon Fraser University

  19. Leveraging ECRAM for Edge Continual Learning

    Jul 22, 2026Nabila Tasnim, Haoran Liu, Qing Cao +1Compute-in-MemoryContinual Learning

    University of Illinois Urbana-Champaign

  20. ThRIve: Thermally Robust CNN Inference via Low-Rank Adaptation in Heterogeneous PIM Architectures

    Jul 19, 2026Vibhanshu Sharma, Pratyush Dhingra, Janardhan Rao Doppa +1Efficient Neural Network InferenceCompute-in-Memory

    Washington State University

  21. NIFA: Nonlinear IMC enhanced FPGA for efficient ML inference

    Jul 16, 2026Jiajun Hu, Ruthwik Reddy Sunketa, Lei Zhao +3Efficient Transformer InferenceCompute-in-Memory

    Arizona State University, Tempe, AZ, USA · Hewlett Packard Enterprise Labs, Fort Collins, CO, USA

  22. Optimizing ML Workload Partitioning between CPUs and CIM Accelerators for Heterogeneous Computing

    Jul 6, 2026Joel Klein, Rebecca Pelke, Roberto Laudani +2Compute-in-MemoryHeterogeneous Computing

    Institute for Communication Technologies and Embedded Systems, RWTH Aachen University, Germany

  23. Multi-Level Resistive Synapses for On-Chip Neural Networks: A Physics-Based Design of a Memristive Crossbar Fabric with Quasi-Continuous Conductance States

    Jun 21, 2026David Alejandro Trejo PizzoEfficient Neural Network InferenceCompute-in-Memory

    Independent researcher.

  24. ReRAM-aware Model Finetuning addressing I-V Non-linearity and Retention Errors

    Jun 16, 2026Ching-Yi Lin, Shamik Kundu, Arnab Raha +1Compute-in-MemoryFine-Tuning

    University of Maryland, College Park, MD, USA · Intel Corporation, 2200 Mission College Blvd, Santa Clara, CA 95054, USA

  25. Neural dynamical systems on ferroelectric compute-in-memory for real-time forecasting

    Jun 15, 2026Keshava Katti, Adithya Selvakumar, Pratik Chaudhari +1Compute-in-MemoryEfficient Neural Network Inference

    Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA

  26. An RRAM-based Hardware Implementation of a Radial Basis Function Neuron for Edge Classifiers

    Jun 2, 2026Georgios Papandroulidakis, Shady Agwa, Themis ProdromakisCompute-in-MemoryEdge ML

    Centre for Electronics Frontiers, Institute of Micro and Nano Systems, School of Engineering, The University of Edinburgh, EH9 3BF, UK.

  27. CRAM-ER: Error-Resilient Spintronic Computational Random Access Memory for Scalable In-Memory Computation

    Jun 1, 2026Sohan Salahuddin Mugdho, Md. Shahedul Hasan, Brahmdutta Dixit +3Efficient Neural Network InferenceCompute-in-Memory

    Electrical and Computer Engineering Iowa State University of Science and Technology Ames, IA, USA · Electrical and Computer Engineering University of Minnesota Twin Cities Minneapolis, MN, USA

  28. LP5X-PIM Sim: A High-Fidelity HW/SW Integrated Simulator for LPDDR5X-PIM

    May 30, 2026SangHoon Cha, Jaewan Choi, Byeongho Kim +3Compute-in-Memory

    Samsung Electronics, South Korea

  29. Memristor-Based Spiking Neural Network Accelerator for Bio-inspired Interception Task

    May 29, 2026Qianhou Qu, Sheng Lu, Liuting Shang +4Efficient Neural Network InferenceCompute-in-Memory

    Department of Electrical Engineering The University of Texas at Arlington Arlington, USA · Department of Electrical Engineering and Computer Science South Dakota State University Brookings, USA

  30. E-ReCON: An Energy- and Resource-Efficient Precision-Configurable Sparse nvCIM Macro for Conventional and Spiking Neural Edge Inference

    May 20, 2026Ankit Kumar Tenwar, Mukul Lokhande, Santosh Kumar VishvakarmaCompute-in-MemoryEdge Inference

    Dept. of Electrical Engineering Indian Institute of Technology Indore Simrol 453552, Indore, India