Application of neuromorphic resistive random access memory in image processing

Abstract

With the increasing demands for processing images and videos at edge terminals, CMOS hardware systems based on conventional Von Neumann architectures are facing challenges in terms of energy consumption, speed, and footprint. Neuromorphic devices, including resistive random access memory with integrated storage-computation characteristic and optoelectronic resistive random access memory with highly integrated in-sensor computing characteristic, show great potentials for image processing applications due to their high similarity with biological neural systems and advantages of high energy efficiency, high integration level, and wide bandwidth. These devices can be used not only to accelerate large numbers of computational tasks in conventional image preprocessing and higher-level image processing algorithms, but also enable to implement highly efficient biomimetic image processing algorithms. In this paper, we first introduce the state-of-the-art neuromorphic resistive random access memory and optoelectronic neuromorphic resistive random access memory, then review the hardware implementation and challenges of in image processing based on these devices, and finally provide perspectives on their future developments.

Amorphous gallium oxide homojunction-based optoelectronic synapse for multi-functional signal processing

“…In sensor-rich applications including intelligent vehicles, static or dynamic video analysis, and micro-robots, the massive amount of raw data collected locally by the sensing terminal must be transferred to local storage and computing units or cloud-based systems, where the inevitable data migration will lead to high power consumption, low processing speed, wide communication bandwidth, and even low security [14−16] . Some optoelectronic devices have been developed to solve the above issues and implement a wide variety of functions in many directions, such as bionic visual receptors [17,18] , filters [19,20] , image recognition [21,22] , polarization-sensitive neuromorphic behavior [23] , and motion tracking [24,25] , and they offer excellent performance and the ability to implement the corresponding applications outstandingly. While most devices can only handle a single task and little exploration of multiple functions has been carried out, which greatly limits the potential of the device and the range of its applications.…”