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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        <foaf:name>Le Gallo, Manuel</foaf:name>
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        <foaf:name>Eleftheriou, Evangelos</foaf:name>
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    <dct:title>Compressed sensing with approximate message passing using in-memory computing</dct:title>
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    <dcat:keyword>Approximate message passing</dcat:keyword>
    <dcat:keyword>Compressed sensing</dcat:keyword>
    <dcat:keyword>In-memory computing</dcat:keyword>
    <dcat:keyword>Phase-change memory</dcat:keyword>
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    <dct:description>&lt;p&gt;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.&lt;/p&gt;</dct:description>
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