Neural models for monitoring and control with applications in automotive domain

Abstract

Development of the state-of-the-art cyber-physical systems (CPS), which incorporate physical and computational components, poses new challenges for research and industry. In order for CPS serve its purpose to make human lives safer, easier, more enjoyable and convenient, both academia and industry needs to develop new methods for control and monitoring of such systems. Neural models are a prospective direction for design of CPS controllers and monitors, and in the thesis we first show, how neural models can be applied in CPS control to quantify the uncertainty of the system. We then show, how digital spiking neural model, called TrueNorth, can be used for runtime monitoring of temporal logic specifications of mission-critical properties. To be able to deliver not only qualitative verdict, but also reason quantitatively, we propose an approach to use the model for computation of arithmetic functions and implement neural monitors for semantics of temporal logic based on circular convolution. In the applied part of the work we show how runtime monitoring can speed up verification and validation phase in automotive electronic development. We identify phases, where runtime monitoring can facilitate both concept and post-silicon verification and testing. To build runtime monitors that are capable to keep up with the speed of the physical sensor, we developed an approach to convert formalized requirements to hardware monitors, which are then synthesized in FPGA. Reuse of the monitors from concept to post-silicon verification phase using high-level synthesis speeds up the testing process and enables long-term requirements evaluation. We illustrated our approach by formalizing, creating hardware monitors and evaluating the results in the lab environment for electrical and timing requirements of industrial SENT and SPC protocols.