ArticleScience advances2023
In-memory analog solution of compressed sensing recovery in one step.
Article in Science advances, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Resistive memory-based neural differential equation solver for score-based diffusion model.Nature communications · 2026Article
- Memristive crossbar array-based hardware framework for compressed sensing and event-driven neuromorphic processing.Nature communications · 2026Article
- Privacy-preserving data analysis using a memristor chip with colocated authentication and processing.Science advances · 2026Article
- In-memory analog computing for non-negative matrix factorization.Nature communications · 2026Article
- Achieving high precision in analog in-memory computing systems.Npj unconventional computing · 2026Review
- An emergent attractor network in a passive resistive switching circuit.Nature communications · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Modern analog computing, by gaining momentum from nonvolatile resistive memory devices, deals with matrix computations. In-memory analog computing has been demonstrated for solving some basic but ordinary matrix problems in one step. Among the more complicated matrix problems, compressed sensing (CS) is a prominent example, whose recovery algorithms feature high-order matrix operations and hardware-unfriendly nonlinear functions. In light of the local competitive algorithm (LCA), here, we present a closed-loop, continuous-time resistive memory circuit for solving CS recovery in one step. Recovery of one-dimensional (1D) sparse signal and 2D compressive images has been experimentally demonstrated, showing elapsed times around few microseconds and normalized mean squared errors of 10
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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.