Published August 29, 2018 | Version v1
Journal article Open

Compressed sensing with approximate message passing using in-memory computing

  • 1. IBM Research - Zurich, 8803 Rüschlikon, Switzerland


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.



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PROJESTOR – PROJECTED MEMRISTOR: A nanoscale device for cognitive computing 682675
European Commission
MNEMOSENE – Computation-in-memory architecture based on resistive devices 780215
European Commission