Esposito, Darjn (2017) VLSI Circuits for Approximate Computing. [Tesi di dottorato]

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Tipologia del documento: Tesi di dottorato
Lingua: English
Titolo: VLSI Circuits for Approximate Computing
Autori:
AutoreEmail
Esposito, Darjndarjesp@gmail.com
Data: 8 Aprile 2017
Numero di pagine: 164
Istituzione: Università degli Studi di Napoli Federico II
Dipartimento: Ingegneria Elettrica e delle Tecnologie dell'Informazione
Dottorato: Information technology and electrical engineering
Ciclo di dottorato: 29
Coordinatore del Corso di dottorato:
nomeemail
Riccio, Danieledaniele.riccio@unina.it
Tutor:
nomeemail
Strollo, Antonio Giuseppe Maria[non definito]
Data: 8 Aprile 2017
Numero di pagine: 164
Parole chiave: Approximate computing; VLSI; digital circuits; arithmetic; precision-scalable units
Settori scientifico-disciplinari del MIUR: Area 09 - Ingegneria industriale e dell'informazione > ING-INF/01 - Elettronica
Depositato il: 09 Mag 2017 14:48
Ultima modifica: 08 Mar 2018 13:28
URI: http://www.fedoa.unina.it/id/eprint/11627
DOI: 10.6093/UNINA/FEDOA/11627

Abstract

Approximate Computing has recently emerged as a promising solution to enhance circuits performance by relaxing the requisite on exact calculations. Multimedia and Machine Learning constitute a typical example of error resilient, albeit compute-intensive, applications. In this dissertation, the design and optimization of approximate fundamental VLSI digital blocks is investigated. In chapter one the theoretical motivations of Approximate Computing, from the VLSI perspective, are discussed. In chapter two my research activity about approximate adders is reported. In this chapter approximate adders for both traditional non-error tolerant applications and error resilient applications are discussed. In chapter three precision-scalable units are investigated. Real-time precision scalability allows adapting the precision level of the unit with the precision requirements of the applications. In this context my research activities regarding approximate Multiply-and-Accumulate and memory units are described. In chapter four a precision-scalable approximate convolver for computer vision applications is discussed. This is composed of both the approximate Multiply-and-Accumulate and memory units, presented in the chapter three.

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