Combining evolutionary algorithms and deep learning for hardware/software interface optimization

Lorenzo Servadei, Edoardo Mosca, Michael Werner, Volkan Esen, Robert Wille, Wolfgang Ecker

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

Abstract

With the advancement of Internet of Things, the cost of System-on-Chips (in terms of area, performance, etc.) becomes increasingly relevant for realizing affordable as well as performant devices. Although System-on-Chips are very diverse with respect to specifications and requirements, some components are ubiquitous. One of them is the Hardware/Software Interface, which serves for controlling communication and interconnected functionalities between Hardware and Software. Motivated by their common use, the implementation of optimized interfaces towards certain costs (in terms of area, performance, etc.) becomes a central problem in the design of embedded systems. In this work we introduce a novel optimization method for minimizing the cost of Hardware/Software Interfaces using Convolutional Neural Networks coupled with Evolutionary Algorithms.

OriginalspracheEnglisch
Titel2019 ACM/IEEE 1st Workshop on Machine Learning for CAD, MLCAD 2019
Herausgeber (Verlag)Institute of Electrical and Electronics Engineers Inc.
ISBN (elektronisch)9781728157580
DOIs
PublikationsstatusVeröffentlicht - Sept. 2019
Veranstaltung1st ACM/IEEE Workshop on Machine Learning for CAD, MLCAD 2019 - Canmore, Kanada
Dauer: 3 Sept. 20194 Sept. 2019

Publikationsreihe

Name2019 ACM/IEEE 1st Workshop on Machine Learning for CAD, MLCAD 2019

Konferenz

Konferenz1st ACM/IEEE Workshop on Machine Learning for CAD, MLCAD 2019
Land/GebietKanada
OrtCanmore
Zeitraum3/09/194/09/19

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