Memristor-Based Signal Processing for Compressed Sensing

Abstract

With the rapid progress of artificial intelligence, various perception networks were constructed to enable Internet of Things (IoT) applications, thereby imposing formidable challenges to communication bandwidth and information security. Memristors, which exhibit powerful analog computing capabilities, emerged as a promising solution expected to address these challenges by enabling the development of the next-generation high-speed digital compressed sensing (CS) technologies for edge computing. However, the mechanisms and fundamental properties of memristors for achieving CS remain unclear, and the underlying principles for selecting different implementation methods based on various application scenarios have yet to be elucidated. A comprehensive overview of memristor-based CS techniques is currently lacking. In this article, we systematically presented CS requirements on device performance and hardware implementation. The relevant models were analyzed and discussed from the mechanism level to elaborate the memristor CS system scientifically. In addition, the method of deploying CS hardware using the powerful signal processing capabilities and unique performance of memristors was further reviewed. Subsequently, the potential of memristors in all-in-one compression and encryption was anticipated. Finally, existing challenges and future outlooks for memristor-based CS systems were discussed.

Extended Higher-Order Elements with Frequency-Doubled Parameters: The Hysteresis Loops Are Always of Type II

“…On the other hand, however, there are applications whose essence lies in the direct exploitation of memristive and similar memory effects [ 25 ]. For example, the integration of memristors with MEMS is a promising way of improving the performance of collecting and processing data from sensors [ 26 , 27 , 28 ]. The contemporary literature describes many examples of the use of nano-memristive devices and nanowires for sensing or detecting temperature [ 29 ], gas [ 30 ], pH [ 31 ], cancer markers [ 32 ], DNA [ 33 , 34 ], UV-light [ 13 ], glucose [ 35 ], proteins [ 36 ], or, for example, for wearable non-contact breath sensing [ 37 ].…”

Research on the Impact of Data Density on Memristor Crossbar Architectures in Neuromorphic Pattern Recognition

“…Hardware implementations of neural computing using memristor crossbars have achieved much success in the last decade [12][13][14][15][16][17]. Since the multiplication and accumula-tion operations can be performed using Kirchoff's law and Ohm's law at the circuit level, the results can be obtained in a single step, leading to a significant improvement in computational speed, energy consumption, and area occupancy [18]. Although the memristor crossbar has many advantages, the implementation of neural computing using memristor crossbar faces many challenges, caused by non-ideal device parameters, for example, programming variation, state-stuck devices, conductance drift, and device variability [19,20].…”