The paper entitled: “Towards dependability-aware design space exploration using genetic algorithms”, written by Quentin Fabry, Ilya Tuzov, Juan-Carlos Ruiz and David de Andres is availabe at ITACA-WIICT 2018 poceedings.
The development of complex digital systems poses some design optimization problems that are today automatically addressed by Electronic Design Automation (EDA) tools. Deducing optimal configurations for EDA tools attending to specific implementation goals is challenging even for simple HW models. In deed, previous research demonstrates that such configurations may have a non-negligible impact on the performance, power-consumption, occupied area and dependability (PPAD) features exhibited by resulting HW implementations. This paper proposes a genetic algorithm to cope with the selection of appropriate configurations of EDA tools. Regardless statistical approaches, this type of algorithms has the benefit of considering all the effects among all configuration flags and their iterations. Consequently, they have a great potential for finding out tool configurations leading to implementations exhibiting optimal PPAD scores. However, it also exists the risk of incurring in very time-consuming design space explorations, which may limit the usability of the approach in practice. Since the behavior of the genetic algorithm will be strongly conditioned by the initially selected population and the mutation, crossover and filtering functions that will be selected for promoting evolution, these parameters must be determined very carefully on a case per case basis. In this publication, we will rely on a multilinear regression model estimating the impact of synthesis flags on the PPAD features exhibited by the implementation of an Intel 8051 microcontroller model. Beyond reported results, this preliminar research show how and to what extend genetic algorithms can be integrated and use in the semi-custom design flow followed today by major HW manufacturers.