Masters Theses

Date of Award

5-2024

Degree Type

Thesis

Degree Name

Master of Science

Major

Computer Engineering

Major Professor

Garrett S. Rose

Committee Members

Garrett S. Rose, James S. Plank, Catherine D. Schuman

Abstract

In neuromorphic computing, a hardware implementation of a spiking neural network is used to provide improved speed and power efficiency over simulations of the networks on a traditional Von Neumann architecture. These hardware implementations employ bio-inspired architecture usually consisting of artificial neurons and synapses implemented in either analog, digital, or mixed-signal circuits. Since these hardware spiking neural networks are designed to support arbitrary networks under the constraints imposed by the available hardware resource, they have to be programmed by off-chip software with awareness of those constraints. The TENNLab research group at the University of Tennessee, Knoxville has recently developed the RAVENS neuroprocessor. A digital implementation of RAVENS designed to support a 64-neuron spiking neural network is being taped out. This thesis presents the work done to improve the software solution for mapping arbitrary spiking neural networks to the RAVENS neuroprocessor.

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