Narrow Results

With the discovery of the memristor devices, most researchers have focused on the simulation and application of memristive characteristics, especially the nonvolatile storage potential of the memristor. However, the fusion of image storage and operation is seldom discussed. Therefore, this paper focuses on the electrical characterization of silver chalcogenide based on memristor devices, which can be used as a synapse to mimic the STDP and SRDP learning rules in neuromorphic systems. Then on this basis, a improved memristive cross array structure is designed, whose storage and operation are set in a single unit. Furthermore, this improved memristive cross array and image processing are combined with adjusting the memristor resistance by synaptic plasticity to achieve the integration of storage and processing. Finally, the effectiveness of the design is verified by the LTSPICE experiments of the nonlinear grayscale transformation and logic operation of the image. The design breaks the gap between the processing and storage in the Von Neumann architecture, which will be helpful to the development of the parallel computing in the future.

The next generation of artificial intelligence systems is generally governed by a new electronic element called memristor. Memristor-based computational system is responsible for confronting memory wall issues in conventional system architecture in the big data era. Complementary Metal Oxide Semiconductor (CMOS) compatibility, nonvolatility and scalability are the important properties of memristor for designing such computing architecture. However, some of the concerns, such as analogue switching and stochasticity, need to be addressed for the use of memristor in novel architecture. Here, we reviewed a number of important scientific works on memristor materials, electrical performance and their integration. In addition, strategies to address the challenges of memristor integration in neuromorphic computing are also being investigated.