This research develops brain-inspired computer chips using memristors, devices that can store and process information simultaneously like biological synapses. By enabling in-memory computing, the technology reduces energy consumption while supporting applications such as autonomous robots and image processing. The work advances efficient hardware for future artificial intelligence systems.

This research develops photonic integrated circuits that compute using light instead of electrons. By creating integrated all-optical transistors and photonic neural networks, the work advances ultra-fast optical computing systems capable of dramatically outperforming conventional electronic processors in speed, efficiency, and future artificial intelligence applications.

This thesis presents the design and verification of a custom RISC-V processor implemented on Field-Programmable Gate Array (FPGA) technology. The project optimized hardware efficiency, achieved stable 50 MHz performance, and enabled software execution using SystemVerilog design and official benchmarks. It demonstrates how open-source hardware enables affordable, customizable computing solutions.