JOURNAL ARTICLE

An efficient read approach for memristive crossbar array

Pravanjan SamantaDev Narayan YadavPartha Pratim DasIndranil Sengupta

Year: 2023 Journal:   Memories - Materials Devices Circuits and Systems Vol: 4 Pages: 100047-100047   Publisher: Elsevier BV

Abstract

Resistive random access memories (ReRAM) have drawn attention of researchers due to their unique properties with applications in in-memory computing, which allows storage and computation in the same unit. This mitigates one of the major limitations in current computing architectures, where for each computation we require to move data from memory to processor or vice versa, which incurs immense amount of energy overheads. Among the various technologies for implementing ReRAM, memristor is considered to be one of the most desirable candidates due to its small size, low power consumption, and high data retention. Such ReRAM systems are often fabricated in the form of crossbar for compact layout. However, they suffer from various challenges, one of the major ones being the sneak-path problem during reading of cell values. The read operation is mostly disturbed by sneak-path currents that can result in incorrect reading of the cell. This paper presents a new approach for reading the cell values in memristive crossbars, which is capable of avoiding erroneous read operations caused by sneak-paths. It also supports parallel operations whereby multiple memristor states can be read in a single cycle. A straightforward approach for reading all the cells in an n×ncrossbar, where the read operation is performed sequentially, requires O(n2)cycles, whereas the proposed approach requires O(n)cycles.

Keywords:
Crossbar switch Memristor Reading (process) Resistive random-access memory Computer science Computation Path (computing) Parallel computing Phase-change memory Computer architecture Computer hardware Electronic engineering Voltage Electrical engineering Algorithm Telecommunications Computer network Engineering

Metrics

3
Cited By
0.50
FWCI (Field Weighted Citation Impact)
32
Refs
0.59
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Advanced Memory and Neural Computing
Physical Sciences →  Engineering →  Electrical and Electronic Engineering
Ferroelectric and Negative Capacitance Devices
Physical Sciences →  Engineering →  Electrical and Electronic Engineering
Neuroscience and Neural Engineering
Life Sciences →  Neuroscience →  Cellular and Molecular Neuroscience
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