Non-Volatile Memory and Associative Learning

Neuromorphic computing is a critical tool in modern problem solving, and non-volatile memory devices like memristors mitigate massive power consumption by transistor-based implementations. Memristors retain a set conductance level even with power off, enabling many practical applications. However, most research studies use idealized simulations, ignoring hardware implementations and non-ideal traits. This project investigates the use of commercially available hardware memristors and their non-ideal properties, to analyze associative learning applications. It demonstrates that non-ideal memristor components are not only feasible for use in machine learning applications, but can actually provide beneficial results when employed in associative memory algorithms