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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.
Index Terms—Approximate message passing, Compressed sensing, In-memory computing, Phase-change memory.
By: Manuel Le Gallo, Abu Sebastian, Giovanni Cherubini, Heiner Giefers, Evangelos Eleftheriou
Published in: IEEE Transactions on Electron Devices, volume 65, (no 10), pages 10.1109/TED.2018.2865352 in 2018
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