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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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<oai_dc:dc xmlns:dc="" xmlns:oai_dc="" xmlns:xsi="" xsi:schemaLocation="">
  <dc:creator>Le Gallo, Manuel</dc:creator>
  <dc:creator>Sebastian, Abu</dc:creator>
  <dc:creator>Cherubini, Giovanni</dc:creator>
  <dc:creator>Giefers, Heiner</dc:creator>
  <dc:creator>Eleftheriou, Evangelos</dc:creator>
  <dc:description>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.</dc:description>
  <dc:source>IEEE Transactions on Electron Devices 65(10) 4304-4312</dc:source>
  <dc:subject>Approximate message passing</dc:subject>
  <dc:subject>Compressed sensing</dc:subject>
  <dc:subject>In-memory computing</dc:subject>
  <dc:subject>Phase-change memory</dc:subject>
  <dc:title>Compressed sensing with approximate message passing using in-memory computing</dc:title>
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