Today performance and operational efficiency of computer systems on digital image processing are exacerbated owing to the increased complexity of image processing. It is also difficult for image processors based on complementary metal–oxide–semiconductor (CMOS) transistors to continuously increase the integration density, causing by their underlying physical restriction and economic costs. However, such obstacles can be eliminated by non-volatile resistive memory technologies (known as memristors), arising from their compacted area, speed, power consumption high efficiency, and in-memory computing capability. This review begins with presenting the image processing methods based on pure algorithm and conventional CMOS-based digital image processing strategies. Subsequently, current issues faced by digital image processing and the strategies adopted for overcoming these issues, are discussed. The state-of-the-art memristor technologies and their challenges in digital image processing applications are also introduced, such as memristor-based image compression, memristor-based edge and line detections, and voice and image recognition using memristors. This review finally envisages the prospects for successful implementation of memristor devices in digital image processing.

Recent popularity to realize image recognition by memristor-based neural network hardware systems has been witnessed owing to their similarities to neurons and synapses. However, the stochastic formation of conductive filaments inside the oxide memristor devices inevitably makes them face some drawbacks, represented by relatively higher power consumption and severer resistance switching variability. In this work, we design and fabricate the Ag/MXene (Ti 3 C 2 ) /SiO 2 /Pt memristor after considering the stronger interactions between Ti 3 C 2 and Ag ions, which lead to a Ti 3 C 2 /SiO 2 structure memristor owning to much lower “SET” voltage and smaller resistance switching fluctuation than pure SiO 2 memristor. Furthermore, the conductances of the Ag/Ti 3 C 2 /SiO 2 /Pt memristor have been modulated by changing the number of the applied programming pulse, and two typical biological behaviors, i.e., long-term potentiation and long-term depression, have been achieved. Finally, device conductances are introduced into an integrated device-to-algorithm framework as synaptic weights, by which the MNIST hand-written digits are recognized with accuracy up to 77.39%.

Carbon-based materials strongly pertain to citizens’ daily life due to their versatile derivatives such as diamond, graphite, fullerenes, carbon nanotube, single-layer graphene, and amorphous carbon (a-C). Compared to other families, a-C exhibits reconfigurable electrical properties by triggering its sp ² –sp ³ transition and vice versa , which can be readily fabricated by conventional film deposition technologies. For above reasons, a-C has been adopted as a promising memristive material and has given birth to several physical and theoretical prototypes. To further help researchers comprehend the physics behind a-C-based memristors and push forward their development, here we first reviewed the classification of a-C-based materials associated with their respective electrical and thermal properties. Subsequently, several a-C -based memristors with different architectures were presented, followed by their respective memristive principles. We also elucidated the state-of-the-art modeling strategies of a-C memristors, and their practical applications on neuromorphic fields were also described. The possible scenarios to further mitigate the physical performances of a-C memristors were eventually discussed, and their future prospect to rival with other memristors was also envisioned.

Due to the increasing demand of high energy-efficient processor for deep neural networks, traditional neural network engines with high-precision weights and activations that usually occupies huge on/off-chip resources with large power consumption are no longer suitable for internet-of-things applications. Binary neural networks (BNNs) reduce memory size and computation complexity, achieving drastically increased energy efficiency. In this paper, an energy-efficient time-domain binary neural network engine is optimized for image recognition, with time-domain accumulation (TD-MAC), timing error detection based adaptive voltage scaling design and the related approximate computing. The proposed key features are: 1) an error-tolerant adaptive voltage scaling system with TD-MAC chain truncation for aggressive power reduction, working from near-threshold to normal voltage; 2) architectural parallelism and data reuse with 100% TD-MAC utilization; 3) low power TD-MAC based on analog delay lines. Fabricated in a 28nm CMOS process, the whole system achieves a maximum 51.5TOPS/W energy efficiency at 0.42V and 25MHz, with 99.6% accuracy on MNIST dataset. When the length of the TD-MAC chain is truncated by configuration, with a 90% accuracy on MNIST and a 150MHz, the proposed BNN achieves a power-saving of 13.2% and a further energy efficiency increasing of 67.6%.

Phase-change random access memory (PCRAM) has been regarded as one of the most promising candidates for future “universal” memory due to its mature fabrication techniques and profitable commercialized prospect. To further improve its physical performance and exploit more application areas, it is imperative to develop a physically realistic model that can closely mimic the electrical, thermal, and phase-change mechanisms of PCRAM. Although plenty of phase-change memory models have been proposed during the last two decades, they were constructed from various physical principles and, thus, targeted for different practical applications. As a result, a comprehensive review to elucidate the state-of-the-art modeling strategies of PCRAMs based on their phase-change mechanisms and application targets remains mysterious. To address this issue, we here reviewed the reported modeling approaches of PCRAMs mainly in terms of the finite-element method, the physics-based analytical model, and compact modeling techniques. For each technique, its adopted strategies to simulate the electrothermal and phase-change processes of phase-change materials at the device level were elaborated, respectively, and their respective merits and drawbacks were also given. The application fields, for which these models suit, were eventually discussed.