Keywords

Evolvable hardware; genetic algorithms; analog; continuous; energy

Abstract

Energy and performance improvements of continuous-time analog-based computation for selected applications offer an avenue to continue improving the computational ability of tomorrow*s electronic devices at current technology scaling limits. However, analog computation is plagued by the difficulty of designing complex computational circuits, programmability, as well as the inherent lack of accuracy and precision when compared to digital implementations. In this thesis, evolutionary algorithm-based techniques are utilized within a reconfigurable analog fabric to realize an automated method of designing analog-based computational circuits while adapting the functional range to improve performance. A Self-Scaling Genetic Algorithm is proposed to adapt solutions to computationally-tractable ranges in hardware-constrained analog reconfigurable fabrics. It operates by utilizing a Particle Swarm Optimization (PSO) algorithm that operates synergistically with a Genetic Algorithm (GA) to adaptively scale and translate the functional range of computational circuits composed of high-level or low-level Computational Analog Elements to improve performance and realize functionality otherwise unobtainable on the intrinsic platform. The technique is demonstrated by evolving square, square-root, cube, and cube-root analog computational circuits on the Cypress PSoC-5LP System-on-Chip. Results indicate that the Self-Scaling Genetic Algorithm improves our error metric on average 7.18-fold, up to 12.92-fold for computational circuits that produce outputs beyond device range. Results were also favorable compared to previous works, which utilized extrinsic evolution of circuits with much greater complexity than was possible on the PSoC-5LP.

Notes

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Graduation Date

2015

Semester

Summer

Advisor

DeMara, Ronald

Degree

Master of Science in Electrical Engineering (M.S.E.E.)

College

College of Engineering and Computer Science

Department

Electrical Engineering and Computing

Degree Program

Engineering and Computer Science

Format

application/pdf

Identifier

CFE0005866

URL

http://purl.fcla.edu/fcla/etd/CFE0005866

Language

English

Release Date

August 2015

Length of Campus-only Access

None

Access Status

Masters Thesis (Open Access)

Subjects

Dissertations, Academic -- Engineering and Computer Science; Engineering and Computer Science -- Dissertations, Academic

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