Manuel Le Gallo, Corey Liam Lammie, et al.
APL Mach. Learn.
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.
Manuel Le Gallo, Corey Liam Lammie, et al.
APL Mach. Learn.
Manuel Le Gallo, Tomas Tuma, et al.
ESSDERC 2016
C.A. Santini, Abu Sebastian, et al.
Nature Communications
Athanasios Vasilopoulos, Julian Buchel, et al.
IEEE T-ED