Evidence map›Paper›PMID 38632304›Full record

ArticleScientific reports2024

Enhanced read resolution in reconfigurable memristive synapses for Spiking Neural Networks.

Hritom Das, Catherine Schuman, Nishith N Chakraborty, Garrett S Rose

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In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Hritom DasDepartment of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN, 37996, USA. hdas@utk.edu.
Catherine SchumanDepartment of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN, 37996, USA.
Nishith N ChakrabortyDepartment of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN, 37996, USA.
Garrett S RoseDepartment of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN, 37996, USA.

Funding

Air Force Research Laboratory FA8750-21-1-1018U.S. Department of Energy DE-SC0022566
6 · The paper itself

Abstract

The synapse is a key element circuit in any memristor-based neuromorphic computing system. A memristor is a two-terminal analog memory device. Memristive synapses suffer from various challenges including high voltage, SET or RESET failure, and READ margin issues that can degrade the distinguishability of stored weights. Enhancing READ resolution is very important to improving the reliability of memristive synapses. Usually, the READ resolution is very small for a memristive synapse with a 4-bit data precision. This work considers a step-by-step analysis to enhance the READ current resolution or the read current difference between two resistance levels for a current-controlled memristor-based synapse. An empirical model is used to characterize the

Indexed as

Approximate computingCurrent-controlledLow powerSpiking Neural NetworkStochastic computing

Identifiers

PMID38632304
PMCPMC11024114

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Registered trials

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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.