Abstract

Many soft applications such as machine learning and probabilistic computational modeling can benefit from approximate but high-performance implementations. In this thesis, we study how Binary decision diagrams (BDDs) can be used to synthesize approximate high-performance implementations from high-level specifications such as program kernels written in a C-like language. We demonstrate the potential of our approach by designing nanoscale crossbars from such approximate Boolean decision diagrams. Our work may be useful in designing massively-parallel approximate crossbar computing systems for application-specific domains such as probabilistic computational modeling.

Notes

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Graduation Date

2018

Semester

Spring

Advisor

Jha, Sumit Kumar

Degree

Master of Science (M.S.)

College

College of Engineering and Computer Science

Department

Computer Science

Degree Program

Computer Science

Format

application/pdf

Identifier

CFE0007414

URL

http://purl.fcla.edu/fcla/etd/CFE0007414

Language

English

Release Date

November 2021

Length of Campus-only Access

3 years

Access Status

Dissertation/Thesis

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