Last September, 17th, Juan Carlos Ruiz took part in the panel “Autonomous driving: safety and security issues”, celebrated in the 5th International Workshop on Critical Automotive Applications: Robustness & Safety (CARS 2019), collocated with EDCC 2019 in Naples, Italy.
Juan Carlos Ruiz has presented the paper entitled “Robustness-aware design space exploration by iteratively augmenting and repairing D-optimal designs”, written by Ilya Tuzov, David De Andrés and Juan Carlos Ruiz.
Design space exploration (DSE) is nowadays of utmost importance to implement HW designs with acceptable levels of performance, power consumption, area and dependability (PPAD). Electronic Design Automation (EDA) tools support the transformation of HW description models into technology-dependent implementations. Although designers can influence this process by tuning the parameters offered by EDA toolkits, determining their proper configuration is a complex and very time-consuming DSE problem rarely addressed from a PPAD perspective. On one hand, the spatial and temporal complexity of considered targets and the level of abstraction of their descriptions pose problems for the rapid execution of fault injection campaigns. On the other hand, the multi-level nature of parameters offered by EDA toolkits lead to an explosion of possible configurations to exercise during experimentation. This paper shows how to combine the D-optimal design of experiments with FPGA-based and statistical fault injection to significantly reduce not only the number of such configurations but also the number of faults to inject and the time required to perform each injection. All of this without compromising the statistical significance of results. The proposal is exemplified through the Xilinx Vivado Design Suite, which integrates one of the FPGA-based EDA toolkits most widely-used today in the industry, and the MC8051 IP core, a synthesizable microcontroller from Oregano Systems.