Tunable Floating-Point for Embedded Machine Learning Algorithms Implementation

Marta Franceschi, Alberto Nannarelli, Maurizio Valle

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

Abstract

The development of embedded and real-time systems for Machine Learning data processing is challenging (e.g. IoT). Low latency, low power consumption and reduced hardware complexity should be the characteristics of such systems. Considering prosthetic applications, which are error-tolerant, a technique that tunes the precision of operands and operations has been chosen for a Machine Learning algorithm used for tactile data processing. This paper presents the implementation of a Tunable Floating-Point (TFP) representation into a Singular-Value Decomposition (SVD) algorithm based on the One-Sided Jacobi method. The TFP representations demonstrate high performance and efficiency improvements of the SVD algorithm.
Original languageEnglish
Title of host publicationProceedings of 2018 15th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design
PublisherIEEE
Publication date2018
Pages89-92
ISBN (Print)9781538651520
DOIs
Publication statusPublished - 2018
Event2018 15th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design - Czech Technical University in Prague, Prague, Czech Republic
Duration: 2 Jul 20185 Jul 2018
Conference number: 15

Conference

Conference2018 15th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design
Number15
LocationCzech Technical University in Prague
CountryCzech Republic
CityPrague
Period02/07/201805/07/2018

Keywords

  • Simulation
  • Power efficiency
  • SVD algorithm

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