In-Memory-Computing Realization with a Photodiode/Memristor Based Vision Sensor.

State-of-the-art IoT technologies request novel design solutions in edge computing, resulting in even more portable and energy-efficient hardware for in-the-field processing tasks. Vision sensors, processors, and hardware accelerators are among the most demanding IoT applications. Resistance switching (RS) two-terminal devices are suitable for resistive RAMs (RRAM), a promising technology to realize storage class memories.

Light sensitive Belousov-Zhabotinsky medium accommodates multiple logic gates.

Computational functionality has been implemented successfully on chemical reactions in living systems. In the case of Belousov-Zhabotinsky (BZ) reaction, this was achieved by using collision-based techniques and by exploiting the light sensitivity of BZ. In order to unveil the computational capacity of the light sensitive BZ medium and the possibility to implement re-configurable logic, the design of multiple logic gates in a fixed BZ reservoir was investigated.

Experimental Study of Artificial Neural Networks Using a Digital Memristor Simulator.

This paper presents a fully digital implementation of a memristor hardware (HW) simulator, as the core of an emulator, based on a behavioral model of voltage-controlled threshold-type bipolar memristors. Compared to other analog solutions, the proposed digital design is compact, easily reconfigurable, demonstrates very good matching with the mathematical model on which it is based, and complies with all the required features for memristor emulators. We validated its functionality using Altera Quartus II and ModelSim tools targeting low-cost yet powerful field-programmable gate array families.