Approximated Canonical Signed Digit for Error Resilient Intelligent Computation

Gian Carlo Cardarilli, Luca Di Nunzio, Rocco Fazzolari, Alberto Nannarelli, M. Re

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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Abstract

Lowering the energy consumption in applications operating on large datasets is one of the main challenges in modern computing. In this context, it is especially important to lower the energy required to transfer data from/to the memory. Usually, this is obtained by applying smart encoding techniques to the data. In this work, we show how to reduce the switching activity in buses and floating-point units by an approximated canonical signed-digit encoder. The precision of the encoding is
programmable and can be chosen depending on the application’s required accuracy.
Original languageEnglish
Title of host publicationProceedings of 53rd Asilomar Conference on Signals, Systems, and Computers
Number of pages5
PublisherIEEE
Publication statusAccepted/In press - 2020
Event2019 Asilomar Conference on Signals, Systems, and Computers - Pacific Grove, United States
Duration: 3 Nov 20196 Nov 2019

Conference

Conference2019 Asilomar Conference on Signals, Systems, and Computers
CountryUnited States
CityPacific Grove
Period03/11/201906/11/2019

Cite this

Cardarilli, G. C., Di Nunzio, L., Fazzolari, R., Nannarelli, A., & Re, M. (Accepted/In press). Approximated Canonical Signed Digit for Error Resilient Intelligent Computation. In Proceedings of 53rd Asilomar Conference on Signals, Systems, and Computers IEEE.