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.

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

Masters Thesis (Campus-only Access)

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