publication . Article . Other literature type . 2018

Compressed Sensing With Approximate Message Passing Using In-Memory Computing

Manuel Le Gallo; Abu Sebastian; Giovanni Cherubini; Heiner Giefers; Evangelos Eleftheriou;
  • Published: 01 Oct 2018 Journal: IEEE Transactions on Electron Devices, volume 65, issue 10, pages 4,304-4,312 (issn: 0018-9383, eissn: 1557-9646, Copyright policy)
In-memory computing is a promising non-von Neumann approach where certain computational tasks are performed within resistive memory units by exploiting their physical attributes. In this paper, we propose a new method for fast and robust compressed sensing (CS) of sparse signals with approximate message passing recovery using in-memory computing. The measurement matrix for CS is encoded in the conductance states of resistive memory devices organized in a crossbar array. In this way, the matrix-vector multiplications associated with both the compression and recovery tasks can be performed by the same crossbar array without intermediate data movements at potential...
free text keywords: Approximate message passing, Compressed sensing, In-memory computing, Phase-change memory, In-Memory Processing, Electronic engineering, Reduction (complexity), Message passing, Time complexity, Robustness (computer science), Matrix (mathematics), Resistive random-access memory, Physics, Topology
Related Organizations
Funded by
Computation-in-memory architecture based on resistive devices
  • Funder: European Commission (EC)
  • Project Code: 780215
  • Funding stream: H2020 | RIA
PROJECTED MEMRISTOR: A nanoscale device for cognitive computing
  • Funder: European Commission (EC)
  • Project Code: 682675
  • Funding stream: H2020 | ERC | ERC-COG
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