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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{
  "DOI": "10.1109/TED.2018.2865352", 
  "container_title": "IEEE Transactions on Electron Devices", 
  "title": "Compressed sensing with approximate message passing using in-memory computing", 
  "issued": {
    "date-parts": [
      [
        2018, 
        8, 
        29
      ]
    ]
  }, 
  "abstract": "<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>", 
  "author": [
    {
      "family": "Le Gallo, Manuel"
    }, 
    {
      "family": "Sebastian, Abu"
    }, 
    {
      "family": "Cherubini, Giovanni"
    }, 
    {
      "family": "Giefers, Heiner"
    }, 
    {
      "family": "Eleftheriou, Evangelos"
    }
  ], 
  "page": "4304-4312", 
  "volume": "65", 
  "type": "article-journal", 
  "issue": "10", 
  "id": "3249877"
}
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