You are here

Self-Scaling Evolution of Analog Computation Circuits

Download pdf | Full Screen View

Date Issued:
2015
Abstract/Description:
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.
Title: Self-Scaling Evolution of Analog Computation Circuits.
17 views
8 downloads
Name(s): Pyle, Steven, Author
DeMara, Ronald, Committee Chair
Vosoughi, Azadeh, Committee Member
Chanda, Debashis, Committee Member
University of Central Florida, Degree Grantor
Type of Resource: text
Date Issued: 2015
Publisher: University of Central Florida
Language(s): English
Abstract/Description: 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.
Identifier: CFE0005866 (IID), ucf:50873 (fedora)
Note(s): 2015-08-01
M.S.E.E.
Engineering and Computer Science, Electrical Engr and Comp Sci
Masters
This record was generated from author submitted information.
Subject(s): Evolvable hardware -- genetic algorithms -- analog -- continuous -- energy
Persistent Link to This Record: http://purl.flvc.org/ucf/fd/CFE0005866
Restrictions on Access: public 2015-08-15
Host Institution: UCF

In Collections