Date of Award

Spring 2020

Rights

Access is available to all users

Document Type

Thesis

Degree Name

Master of Science (MS) in Computer Science

Department

Computer Science

Abstract

This research explores the use of heterogeneous computing platforms for use in machine learning as well as different neural network architectures. These platforms and architectures can be used to accelerate the complex operations that are required for machine learning, more specifically neural networks. The use of different architectures, implementing different types of numbering and mathematics systems is explored in hopes of accelerating mathematical functions. The heterogeneous computing platform explored in this thesis is a Field Programmable Gate Arrays (FPGA), specifically a SoC/FPGA which is a ARM CPU and a FPGA in the same chip. FPGAs are unique because they are low power, high customizable hardware with bit level control. A new numbering system, called Tally, can be simulated in software but only implemented in hardware with a FPGA, without time consuming and expensive ASIC (application specific integrated circuit) being designed. This thesis explores two different types of neural networks are explored the first a simple XOR (exclusive OR) gate neural network tested in the Tally system, 16-bit fixed point and 32-bit floating point. The MNIST (handwritten numbers) dataset is used with a pre-trained multi-layer perceptron with both 16-bit Fixed Point and 32-bit Floating Point numbers. This study will act as a preliminary exploration of the Tally System and an exercise in learning implementation of neural networks in hardware.

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Share

COinS