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

### Citation Style Language JSON Export

{
"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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