Combining Evolutionary Algorithms and Deep Learning for Hardware/Software Interface Optimization

  • Lorenzo Servadei (Speaker)
  • Edorado Mosca (Speaker)
  • J.-H. Lee (Speaker)
  • J. Yang (Speaker)
  • Robert Wille (Speaker)
  • Wolfgang Ecker (Speaker)

Activity: Talk or presentationContributed talkscience-to-science

Description

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.
Period03 Sept 2019
Event titleWorkshop on Machine Learning for CAD (MLCAD)
Event typeConference
LocationCanadaShow on map

Fields of science

  • 202 Electrical Engineering, Electronics, Information Engineering
  • 102 Computer Sciences

JKU Focus areas

  • Digital Transformation