Efficient modeling and computation methods for robust AMS system design

This dissertation copes with the challenge regarding the development of model based design tools that better support the mixed analog and digital parts design of embedded systems. It focuses on the conception of efficient modeling and simulation methods that adequately support emerging system level design methodologies. Starting with a deep analysis of the design activities, many weak points of today’s system level design tools were captured. After considering the modeling and simulation of power electronic circuits for designing low energy embedded systems, a novel signal model that efficiently captures the dynamic behavior of analog and digital circuits is proposed and utilized for the development of computation methods that enable the fast and accurate system level simulation of AMS systems. In order to support a stepwise system design refinement which is based on the essential system properties, behavior computation methods for linear and nonlinear analog circuits based on the novel signal model are presented and compared regarding the performance, accuracy and stability with existing numerical and analytical methods for circuit simulation. The novel signal model in combination with the method proposed to efficiently cope with the interaction of analog and digital circuits as well as the new method for digital circuit simulation are the key contributions of this dissertation because they allow the concurrent state and event based simulation of analog and digital circuits. Using a synchronous data flow model of computation for scheduling the execution of the analog and digital model parts, very fast AMS system simulations are carried out. As the best behavior abstraction for analog and digital circuits may be selected without the need of changing component interfaces, the implementation, validation and verification of AMS systems take advantage of the novel mixed signal representation. Changes on the modeling abstraction level do not affect the experiment setup. The second part of this work deals with the robust design of AMS systems and its verification. After defining a mixed sensitivity based robustness evaluation index for AMS control systems, a general robust design method leading to optimal controller tuning is presented. To avoid over-conservative AMS system designs, the proposed robust design optimization method considers parametric uncertainty and nonlinear model characteristics. The system properties in the frequency domain needed to evaluate the system robustness during parameter optimization are obtained from the proposed signal model. Further advantages of the presented signal model for the computation of control system performance evaluation indexes in the time domain are also investigated in combination with range arithmetic. A novel approach for capturing parameter correlations in range arithmetic based circuit behavior computation is proposed as a step towards a holistic modeling method for the robust design of AMS systems. The several modeling and computation methods proposed to improve the support of design methodologies and tools for AMS system are validated and evaluated in the course of this dissertation considering many aspects of the modeling, simulation, design and verification of a low power embedded system implementing Adaptive Voltage and Frequency Scaling (AVFS) for energy saving