Paper Info
Title
Compressed Sensing By Phase Change Memories: Coping With Encoder Non-Linearities
Abstract
Several recent works have shown the advantages of using phase-change memory (PCM) in developing brain-inspired computing approaches. In particular, PCM cells have been applied to the direct computation of matrix-vector multiplications in the analog domain. However, the intrinsic nonlinearity of these cells with respect to the applied voltage is detrimental. In this paper we consider a PCM array as the encoder in a Compressed Sensing (CS) acquisition system, and investigate the effect of the non-linearity of the cells. We introduce a CS decoding strategy that is able to compensate for PCM nonlinearities by means of an iterative approach. At each step, the current signal estimate is used to approximate the average behaviour of the PCM cells used in the encoder. Monte Carlo simulations relying on a PCM model extracted from an STMicrolectronics 90 nm BCD chip validate the performance of the algorithm with various degrees of nonlinearities, showing up to 35 dB increase in median performance as compared to standard decoding procedures.
Year
DOI
Venue
2021
10.1109/ISCAS51556.2021.9401176
2021 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS)
DocType
ISSN
Citations 
Conference
0271-4302
0
PageRank 
References 
Authors
0.34
0
11
Name
Order
Citations
PageRank
Carmine Paolino102.03
Alessio Antolini201.01
Fabio Pareschi322632.16
Mauro Mangia412520.94
Riccardo Rovatti537754.32
Eleonora Franchi Scarselli600.34
Antonio Gnudi75823.00
Gianluca Setti800.34
Roberto Canegallo9759.96
Marcella Carissimi1053.24
Marco Pasotti11103.68