ArticleNature communications2024
Memristor-based storage system with convolutional autoencoder-based image compression network.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Deterministic Switching-Path Engineering of CMOS-Integrated 2D Memristors for Neuromorphic Computing.Small (Weinheim an der Bergstrasse, Germany) · 2026Article
- Programmable Speech Recognition Based on Cu/CuBiSeACS omega · 2026Article
- Photonic computing enabled end-to-end image compression and reconstruction with adjustable ratio and high-fidelity.Nature communications · 2026Article
- A hardware-adaptive learning algorithm for superlinear-capacity associative memory on memristor crossbars.Nature communications · 2026Article
- Atomic Defects in Layered Transition Metal Dichalcogenides for Sustainable Energy Storage and the Intelligent Trends in Data Analytics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Error-aware probabilistic training for memristive neural networks.Nature communications · 2025Article
- Hybrid functional 3D artificial synapses for convolution and reinforcement learning.Science advances · 2025Article
- Ultrahigh-precision analog computing using memory-switching geometric ratio of transistors.Science advances · 2025Article
- Two-dimensional materials based two-transistor-two-resistor synaptic kernel for efficient neuromorphic computing.Nature communications · 2025Article
- Optimization strategy of the emerging memristors: From material preparation to device applications.iScience · 2024Review
- High-Quality Image Compression Algorithm Design Based on Unsupervised Learning.Sensors (Basel, Switzerland) · 2024Article
- Research on low-power driving fatigue monitoring method based on spiking neural network.Experimental brain research · 2024Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
The exponential growth of various complex images is putting tremendous pressure on storage systems. Here, we propose a memristor-based storage system with an integrated near-storage in-memory computing-based convolutional autoencoder compression network to boost the energy efficiency and speed of the image compression/retrieval and improve the storage density. We adopt the 4-bit memristor arrays to experimentally demonstrate the functions of the system. We propose a step-by-step quantization aware training scheme and an equivalent transformation for transpose convolution to improve the system performance. The system exhibits a high (>33 dB) peak signal-to-noise ratio in the compression and decompression of the ImageNet and Kodak24 datasets. Benchmark comparison results show that the 4-bit memristor-based storage system could reduce the latency and energy consumption by over 20×/5.6× and 180×/91×, respectively, compared with the server-grade central processing unit-based/the graphics processing unit-based processing system, and improve the storage density by more than 3 times.
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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.