Analog Computing

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27 papers3 papers in the last four weeks, down 25% on the four weeks before. 0.0% of all new papers.

Enabling Retraining-Free Accurate Inference in Analog Compute in Memory Accelerators
Nov 5, 2026George Karfakis, Puneet GuptaCompute-in-Memory Neural Network Hardware Acceleration
Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA.
Analog and hybrid computing in the 21st century
Oct 30, 2026Bernd Ulmann, Olivier Bournez, Corey J. Maley +19Analog Computing Scientific Computing
FOM University of Applied Sciences for Economics and Management, 60323 Frankfurt a. M., Germany · anabrid GmbH, 60486 Frankfurt a. M., Germany · LIX, CNRS, Ecole Polytechnique, Institut Polytechnique de Paris, 91120 Palaiseau, France +18
Functional Superoscillations in a Wave Processor
Oct 25, 2026Yuntian Wang, Md Sadman Sakib Rahman, Aydogan OzcanAnalog Computing Photonic Computing
Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA · Bioengineering Department, University of California, Los Angeles, CA, 90095, USA · California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, 90095, USA
Lock-in EP: An In-Situ Training Algorithm for Oscillatory Hardware
Oct 5, 2026Sowjanya Tammali, Wilkie Olin-AmmentorpAnalog Computing Equilibrium Propagation
Computer Science, Missouri University of Science and Technology, 500 West 15th Street, Rolla, 65409, MO, USA. · Mathematics & Computer Science Division, Argonne National Laboratory, 9700 S. Cass Avenue, Lemont, 60439, IL, USA.
Making Analog Training Scale: Co-Designing Mapping, Optimizer, and Converters
Sep 29, 2026Zhaoxian Wu, Tayfun Gokmen, Omobayode Fagbohungbe +2Mixed-Precision Training Analog Computing
Cornell Tech and Cornell University · IBM T. J. Watson Research Center · Sandia National Laboratories
Analog Computing revisited: A fully analog and minimalistic Damage Detector for Ultrasonic Testing enabling Material-Integrated Structural Health Monitoring
Sep 28, 2026Stefan BosseAnalog Computing Fault Detection
University of Koblenz, Practical Computer Science, Koblenz, Germany
Analog-Friendly Predictive Coding without Activation Derivatives
Sep 26, 2026Francesco InnocentiAnalog Computing Predictive Coding
Brain Network Dynamics Unit University of Oxford, UK
Beyond Noise: Understanding and Overcoming Temperature Effects in Analog DNN Inference
Sep 14, 2026Niklas Summ, Xiao Wang, Hendrik Borras +2Neural Network Robustness Model Calibration
Hardware and Artificial Intelligence Lab, Institute of Computer Engineering, Heidelberg University, Germany
RACE-AIMC: Selective Inference for Heterogeneous Analog In-Memory Accelerators at the Edge
Sep 2, 2026Osama Yousuf, Martin Lueker-BodenEfficient Neural Network Inference Compute-in-Memory
WD Research San Jose, CA, USA
Compute-in-Memory Attention: A Time-Domain Analog Softmax Circuit with RC-Tunable Temperature
Sep 2, 2026Ankur Singh, Ashish Gautam, Shruti R. Kulkarni +1Softmax Attention Analog Computing
Department of Electrical and Computer Engineering, University of Wisconsin–Madison, Madison, WI, USA. · ORNL Oak Ridge, Tennessee, USA.
ASSERT: Adaptive Stochastic Sampling for Robust Diffusion Models on Analog Compute-in-Memory Hardware
Sep 1, 2026Yuannuo Feng, Yizhe Chen, Wenshuai Yao +4Compute-in-Memory Diffusion 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
RevCRN: Reversible Analog Computation using Chemical Reaction Networks
Aug 11, 2026Saptarshi Biswas, James I. Lathrop, Rana D. ParshadAnalog Computing
Department of Mathematics, Iowa State University, Ames, IA, USA
NANQ: Noise-Floor-Aware Mixed-Precision Non-Uniform Quantization for Analog Compute-in-Memory
Aug 3, 2026Yizhe Chen, Wenshuai Yao, Saiya Wang +6Mixed-Precision Quantization Compute-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
Intrinsic-Noise Consolidation: A Doob-Barrier-Conditioned Diffusion Turns Analog Device Noise into a Continual-Learning Resource
Jul 8, 2026Gunner Levi HoweContinual Learning Stability-Plasticity Dilemma
Generative Models on Analog Hardware with Dynamics
Jun 25, 2026Yu-Neng Wang, Sara AchourAnalog Computing Generative Modeling
Stanford University · USA
Low-power analogue neural networks with trainable nonlinear connections for continuous control
Jun 21, 2026Ian T. Vidamour, Fernando Aguirre, Thomas J. Hayward +13Efficient Neural Network Inference Kolmogorov-Arnold Networks
School of Computer Science, University of Sheffield, Sheffield, S1 4DP, United Kingdom · Intrinsic Semiconductor Technologies, London, United Kingdom · School of Chemical, Biological, and Materials Science Engineering, University of Sheffield, Sheffield, S1 3JD, United Kingdom +5
Neural dynamical systems on ferroelectric compute-in-memory for real-time forecasting
Jun 15, 2026Keshava Katti, Adithya Selvakumar, Pratik Chaudhari +1Compute-in-Memory Efficient Neural Network Inference
Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA
Positional Encoding in the Context of Memristor-Based Analog Computation for Automatic Speech Recognition
Jun 11, 2026Benedikt Hilmes, Nick Rossenbach, Ralf SchlüterAutomatic Speech Recognition Positional Encoding
Machine Learning and Human Language Technology Group, Faculty of Computer Science, RWTH Aachen University, Aachen, Germany
Modern analog computing for solving differential and matrix equations
Jun 11, 2026Zhong Sun, Piergiulio Mannocci, Manuel Le Gallo +1Analog Computing ODE Solving
Institute for Artificial Intelligence, School of Integrated Circuits, Peking University, Beijing Advanced Innovation Center for Integrated Circuits, Beijing 100871, China. · 2Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Piazza Leonardo da Vinci 32, Milano, 20133, Italy. · 3IBM Research Europe, Rüschlikon, Switzerland.
Beyond Gradient Descent: Adam for Analog Ising Machines
Jun 2, 2026Stijn Van Vooren, Guy Van der Sande, Guy VerschaffeltCombinatorial Optimization Adaptive Gradient Methods
Applied Physics research group, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium∗
Hardware-Software Co-Design of Scalable, Energy-Efficient Analog Recurrent Computations
May 12, 2026Arthur Fyon, Julien Brandoit, Loris Mendolia +3Energy-Efficient ML Keyword Spotting
University of Liège Liège, Belgium
Thermodynamic Diffusion Inference with Minimal Digital Conditioning
Apr 15, 2026Aditi DeAnalog Computing Diffusion Models
Analog Optical Inference on Million-Record Mortgage Data
Apr 14, 2026Sofia Berloff, Pavel Koptev, Konstantin MalkovAnalog Computing Photonic Computing
Department of Mathematics, University of York, York, UK · ai1Technologies, 27 Beach Rd Suite 5A Monmouth Beach, NJ 07750, USA
Neuromorphic Computing for Low-Power Artificial Intelligence
Apr 6, 2026Keshava Katti, Pratik Chaudhari, Deep JariwalaAnalog Computing Neuromorphic Computing
Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, USA.
Dynamic Symmetric Point Tracking: Tackling Non-ideal Reference in Analog In-memory Training
Feb 24, 2026Quan Xiao, Jindan Li, Zhaoxian Wu +2Compute-in-Memory Analog Computing
Department of Electrical and Computer Engineering, Cornell University, New York, NY · IBM T. J. Watson Research Center, Yorktown Heights, NY · Rensselaer Polytechnic Institute, Troy, NY
DARTH-PUM: A Hybrid Processing-Using-Memory Architecture
Feb 17, 2026Ryan Wong, Ben Feinberg, Saugata GhoseCompute-in-Memory Analog Computing
Univ. of Illinois Urbana-Champaign, Urbana, IL, USA · Sandia National Laboratories, Albuquerque, NM, USA
Physical Analogue Kolmogorov-Arnold Networks based on Reconfigurable Nonlinear-Processing Units
Feb 7, 2026Manuel Escudero, Mohamadreza Zolfagharinejad, Sjoerd van den Belt +2Efficient Neural Network Inference Neural Network Hardware Acceleration
1NanoElectronics Group, MESA+ Institute and BRAINS Center for Brain-Inspired Computing, University of Twente, P.O. Box 217, Enschede,2026 7500 AE, The Netherlands. · 2CAESUniversity GroupofandTwente,BRAINSEnschede,Center 7500for Brain-InspiredAE, The Netherlands.Computing · Institute of Physics, University of Münster, Münster, 48149 Germany.

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Enabling Retraining-Free Accurate Inference in Analog Compute in Memory Accelerators

George Karfakis, Puneet Gupta

Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA.

Compute-in-Memory Neural Network Hardware Acceleration Efficient Neural Network Inference Analog Computing

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Analog compute-in-memory (CIM) accelerators compute matrix-vector products directly inside memory arrays, eliminating weight movement and promising order-of-magnitude gains in inference efficiency. However, converter and device errors make pretrained models unusable on these arrays, and hardware-aware retraining is costly. We enable retraining-free, accurate inference of pretrained models on simple analog arrays with low-cost, shared calibration. Our key observation is that analog error depends on how each layer is represented on the array, and that this representation can be changed offline without changing the computation. We center the inputs and apply Hadamard rotations designed for analog arrays, which keep shared reference errors spread and confine input mixing to each array. Across CNNs, vision transformers and LLMs up to 14B parameters on PCM and ReRAM, the method brings vision models and larger LLMs within a few points of floating-point accuracy and matches or approaches published hardware-aware training without training any weights. On ReRAM with 6-bit ADCs, it restores ViT-B/16 from 37.4% to 82.9% and Phi-4 from 27.0% to 72.2%, both within 2.2 points of floating-point accuracy.