Journal article Open Access

Compressed sensing with approximate message passing using in-memory computing

Le Gallo, Manuel; Sebastian, Abu; Cherubini, Giovanni; Giefers, Heiner; Eleftheriou, Evangelos


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{
  "description": "<p>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 of sparse signals with approximate message passing recovery using in-memory computing. The measurement matrix for compressed sensing is encoded in the conductance states of resistive memory devices organized in a crossbar array. 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 O(1) time complexity. For a signal of size N, the proposed method achieves a potential O(N)-fold recovery complexity reduction compared with a standard software approach. We show the array-level robustness of the scheme through large-scale experimental demonstrations using more than 256k phase-change memory devices.</p>", 
  "license": "https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode", 
  "creator": [
    {
      "affiliation": "IBM Research - Zurich, 8803 R\u00fcschlikon, Switzerland", 
      "@type": "Person", 
      "name": "Le Gallo, Manuel"
    }, 
    {
      "affiliation": "IBM Research - Zurich, 8803 R\u00fcschlikon, Switzerland", 
      "@type": "Person", 
      "name": "Sebastian, Abu"
    }, 
    {
      "affiliation": "IBM Research - Zurich, 8803 R\u00fcschlikon, Switzerland", 
      "@type": "Person", 
      "name": "Cherubini, Giovanni"
    }, 
    {
      "@type": "Person", 
      "name": "Giefers, Heiner"
    }, 
    {
      "@type": "Person", 
      "name": "Eleftheriou, Evangelos"
    }
  ], 
  "headline": "Compressed sensing with approximate message passing using in-memory computing", 
  "image": "https://zenodo.org/static/img/logos/zenodo-gradient-round.svg", 
  "datePublished": "2018-08-29", 
  "url": "https://zenodo.org/record/3249877", 
  "keywords": [
    "Approximate message passing", 
    "Compressed sensing", 
    "In-memory computing", 
    "Phase-change memory"
  ], 
  "@context": "https://schema.org/", 
  "identifier": "https://doi.org/10.1109/TED.2018.2865352", 
  "@id": "https://doi.org/10.1109/TED.2018.2865352", 
  "@type": "ScholarlyArticle", 
  "name": "Compressed sensing with approximate message passing using in-memory computing"
}
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