Print Email Facebook Twitter Application of Emerging Memory Technologies for Spiking Neural Networks Title Application of Emerging Memory Technologies for Spiking Neural Networks Author Buis, Jan Maarten (TU Delft Electrical Engineering, Mathematics and Computer Science; TU Delft Circuits and Systems) Contributor van Leuken, T.G.R.M. (mentor) Mandloi, N.K. (graduation committee) Degree granting institution Delft University of Technology Programme Computer Engineering Date 2022-03-29 Abstract 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. Subject memristorfefetneuromorphicspiking neural networks To reference this document use: http://resolver.tudelft.nl/uuid:997eacf7-f3af-4482-9073-e86e6900592b Part of collection Student theses Document type master thesis Rights © 2022 Jan Maarten Buis Files PDF Master_thesis_JMBuis.pdf 910.64 KB Close viewer /islandora/object/uuid:997eacf7-f3af-4482-9073-e86e6900592b/datastream/OBJ/view