Memristor-based signal processing for edge computing

Abstract

The rapid growth of the Internet of Things (IoTs) has resulted in an explosive increase in data, and thus has raised new challenges for data processing units. Edge computing, which settles signal processing and computing tasks at the edge of networks rather than uploading data to the cloud, can reduce the amount of data for transmission and is a promising solution to address the challenges. One of the potential candidates for edge computing is a memristor, an emerging nonvolatile memory device that has the capability of in-memory computing. In this article, from the perspective of edge computing, we review recent progress on memristor-based signal processing methods, especially on the aspects of signal preprocessing and feature extraction. Then, we describe memristor-based signal classification and regression, and end-to-end signal processing. In all these applications, memristors serve as critical accelerators to greatly improve the overall system performance, such as power efficiency and processing speed. Finally, we discuss existing challenges and future outlooks for memristor-based signal processing systems.

Toward memristive in-memory computing: principles and applications

“…Traditional digital signal processing methods usually use analog signal sensors and digital processing units which are physically segmented from each other, leading to high latency and energy consumption for data transferring. IMCbased image processing has shown significant advantages not only in the parallel processing of MVM operations but also in taking advantage of the analog computing nature by reducing the distance between the signal sensing and processing in abundant techniques [121][122][123][124]. Furthermore, many new types of memristors, as well as other emerging nanodevices, have been developed as visual [125,126], auditory [127], and olfactory [128] sensors, etc., generating excitation from the applied signals and processing them in situ, and greatly improving the compactness, processing speed, and energy efficiency.…”

Energy-efficient high-fidelity image reconstruction with memristor arrays for medical diagnosis

“…1a), showing appealing advantages in terms of energy efficiency and speed compared to conventional hardware [27][28][29] . Besides ANNs, there have also been attempts to use memristor arrays for implementing classic signal processing algorithms 30 , such as finite impulse response (FIR) filter 31 and discrete Fourier transformation (DFT) 32,33 , which has the potential to significantly accelerate medical image reconstruction speed and reduce energy consumption. In both applications, the most computationally intensive computations are vector-matrix multiplication (VMM); however, their actual implementations on memristor arrays are quite different in two aspects.…”

“…The left atrium MRI dataset used in our work is one of the ten datasets22,23,24,26,29,30) are used to further prove the consistent performance of MIR, as shown in Figs. Here, to simulate the MRI image reconstruction process, we first divide left atrium MRI image into several 64×64 patches for 64-point DFT computations on our memristor array.…”

Energy-efficient high-fidelity image reconstruction with memristor arrays for medical diagnosis

“…1a), showing appealing advantages in terms of energy efficiency and speed compared to conventional hardware [30][31][32] . Besides ANNs, there have also been attempts to use memristor arrays for implementing classic signal processing algorithms 33 , such as finite impulse response (FIR) filter 34 and discrete Fourier transformation (DFT) 35,36 , which has the potential to significantly accelerate medical image reconstruction speed and reduce energy consumption. In both applications, the most computationally intensive computations are vector-matrix multiplication (VMM); however, their actual implementations on memristor arrays are quite different in two aspects.…”