Application of Emerging Memory Technologies for Spiking Neural Networks

Application of Emerging Memory Technologies for Spiking Neural Networks

Buis, Jan Maarten (TU Delft Electrical Engineering, Mathematics and Computer Science; TU Delft Circuits and Systems)

van Leuken, T.G.R.M. (mentor)
Mandloi, N.K. (graduation committee)

Delft University of Technology

Computer Engineering

2022-03-29

Renewed interest in memory technologies such as memristors and ferroelectric devices can provide opportunities for traditional and non-traditional computing systems alike. To make versatile, reprogrammable AI hardware possible, neuromorphic systems are in need of a low-power, non-volatile and analog memory solution to store the weights of the spiking neural network (SNN). In addition to being used for memory, memristive memory can be read out passively and thus also replaces digital-to-analog circuitry.
In this thesis, two solutions are proposed: one is based on a generalized memristor, the other is based on ferroelectric memory. Both solutions are implemented and simulated in SystemC AMS and tested with a SNN. As a final test, both memory solutions are integrated into a full-sized SNN and simulated against the MNIST dataset. The simulation results validate the capabilities of memristive and ferroelectric memory when it comes to providing a sensible weight storage solution for neuromorphic systems.

memristor
fefet
neuromorphic
spiking neural networks

http://resolver.tudelft.nl/uuid:997eacf7-f3af-4482-9073-e86e6900592b

Student theses

master thesis

© 2022 Jan Maarten Buis