ArticleNature communications2020
Accurate deep neural network inference using computational phase-change memory.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 72 papers.
What it found
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Who cites it
72 citing papers in PubMed.
- Deterministic Switching-Path Engineering of CMOS-Integrated 2D Memristors for Neuromorphic Computing.Small (Weinheim an der Bergstrasse, Germany) · 2026Article
- Analysis and Applications of Neuromorphic Memristors in Artificial Intelligence Computing.Nano-micro letters · 2026Review
- Adaptive Redox Resistive Memory Programming for Efficient and Robust Class-Incremental Learning.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Broadband encoding and high-speed probabilistic bit generation with integrated microwave neurons.Nature communications · 2026Article
- Article
- In-Sensor-Memory Computing for Post-Von Neumann Intelligence: A Perspective.Nano-micro letters · 2026Article
- Deep neural network inference on an integrated, reconfigurable photonic tensor processor.Nature communications · 2026Article
- Antiferroelectric polarization enabling physical activation in CuBiPNature communications · 2026Article
- Supernetwork-based efficient mapping of deep learning applications to mixed-precision hardware using model adaptation.Nature communications · 2026Article
- Efficient next-generation reservoir computing: An analog in-memory implementation using memristor crossbar arrays.iScience · 2026Article
- A reconfigurable photosensitive split-floating-gate memory for neuromorphic computing and nonlinear activation.Nature communications · 2026Article
- Pruning random resistive memory for optimizing analog AI.Nature communications · 2026Article
- Error-aware probabilistic training for memristive neural networks.Nature communications · 2025Article
- Brain-Inspired In-Memory Data Pruning and Computing with TaOAdvanced materials (Deerfield Beach, Fla.) · 2025Article
- Memristive In-Memory Object Detection with 128 Mb C-Doped GeAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- High-Entropy Oxide Memristors for Neuromorphic Computing: From Material Engineering to Functional Integration.Nano-micro letters · 2025Review
- A near-threshold memristive computing-in-memory engine for edge intelligence.Nature communications · 2025Article
- Phase-Change Memory for In-Memory Computing.Chemical reviews · 2025Review
- The Role of Phase-Change Memory in Edge Computing and Analog In-Memory Computing: An Overview of Recent Research Contributions and Future Challenges.Sensors (Basel, Switzerland) · 2025Review
- Memristive Bellman solver for decision-making.Nature communications · 2025Article
12 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In-memory computing using resistive memory devices is a promising non-von Neumann approach for making energy-efficient deep learning inference hardware. However, due to device variability and noise, the network needs to be trained in a specific way so that transferring the digitally trained weights to the analog resistive memory devices will not result in significant loss of accuracy. Here, we introduce a methodology to train ResNet-type convolutional neural networks that results in no appreciable accuracy loss when transferring weights to phase-change memory (PCM) devices. We also propose a compensation technique that exploits the batch normalization parameters to improve the accuracy retention over time. We achieve a classification accuracy of 93.7% on CIFAR-10 and a top-1 accuracy of 71.6% on ImageNet benchmarks after mapping the trained weights to PCM. Our hardware results on CIFAR-10 with ResNet-32 demonstrate an accuracy above 93.5% retained over a one-day period, where each of the 361,722 synaptic weights is programmed on just two PCM devices organized in a differential configuration.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.