August 29, 2018 Journal article Open Access

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>", 
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    "keywords": [
      "Approximate message passing", 
      "Compressed sensing", 
      "In-memory computing", 
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    "publication_date": "2018-08-29", 
    "creators": [
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        "affiliation": "IBM Research - Zurich, 8803 R\u00fcschlikon, Switzerland", 
        "name": "Le Gallo, Manuel"
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        "affiliation": "IBM Research - Zurich, 8803 R\u00fcschlikon, Switzerland", 
        "name": "Sebastian, Abu"
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      {
        "affiliation": "IBM Research - Zurich, 8803 R\u00fcschlikon, Switzerland", 
        "name": "Cherubini, Giovanni"
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      {
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