Joule heating effects in nanoscale carbon-based memory devices
Tobias Bachmann, A.M. Alexeev, et al.
NMDC 2016
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.
Tobias Bachmann, A.M. Alexeev, et al.
NMDC 2016
Christoph Hagleitner, Tony Bonaccio, et al.
IEEE Journal of Solid-State Circuits
Teddy Loeliger, Peter Bachtold, et al.
ESSCIRC 2002
Simeon Furrer, Patrick Ebermann, et al.
IEEE Transactions on Magnetics