51 research outputs found
Speeding-Up Expensive Evaluations in High-Level Synthesis Using Solution Modeling and Fitness Inheritance
High-Level Synthesis (HLS) is the process of developing digital circuits from behavioral specifications. It involves three interdependent and NP-complete optimization problems: (i) the operation scheduling, (ii) the resource allocation, and (iii) the controller synthesis. Evolutionary Algorithms have been already effectively applied to HLS to find good solution in presence of conflicting design objectives. In this paper, we present an evolutionary approach to HLS that extends previous works in three respects: (i) we exploit the NSGA-II, a multi-objective genetic algorithm, to fully automate the design space exploration without the need of any human intervention, (ii) we replace the expensive evaluation process of candidate solutions with a quite accurate regression model, and (iii) we reduce the number of evaluations with a fitness inheritance scheme. We tested our approach on several benchmark problems. Our results suggest that all the enhancements introduced improve the overall performance of the evolutionary search
Impact of real-time ultrasound guidance on complications of percutaneous dilatational tracheostomy: a propensity score analysis
A quantitative evaluation of a Network on Chip design flow for multi-core consumer multimedia applications
Use of ultrasound guidance to improve the safety of percutaneous dilatational tracheostomy: a literature review
Das toxische Inhalationstrauma beim Brandverletzten im Vergleich zur militärmedizinsichen Wirkung von Kampfgas
Operative Versorgung schwerer palmarer Verbrennungsontrakturen von Kleinkindern im Afghanistan-Einsatz
Neue Informationstechnologien im Stationsalltag: Entwicklung einer Smartphone-App für unfallchirurgisch-orthopädische Assistenten
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