Compressed Sensing With Approximate Message Passing Using In-Memory Computing

Abstract

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 (CS) of sparse signals with approximate message passing recovery using in-memory computing. The measurement matrix for CS is encoded in the conductance states of resistive memory devices organized in a crossbar array. In 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.