As CMOS technology scales down into the deep-submicron domain, the cost of design, complexity and customization for Systems-On-Chip (SoCs) is rapidly increasing due to the inefficiency of traditional CAD tools. In this paper we present a new interactive refinement algorithm in high-level synthesis, based on dynamic programming, which maximizes resource optimization in data path. We start by quantifying the properties of the given application C code in terms of control data flow graph (CDFG), available parallelism and other metrics. We then apply designer guided constraints to a data path refinement algorithm for an initial data path. It attempts to reduce the number of the most expensive components while meeting the constraints. The experimental results show that not only the refined data path outperforms data paths refined by other heuristic methods, but also presents lower cost, less overhead and can be generated in less time.
In this thesis, a new inference-based solution to stochastic optimal control (SOC) for general nonlinear systems is developed. This novel method applies to standard SOC problem, as well as robust and risk-seeking variations. The presented approach unifies many existing works, and makes possible, inference-based approximations to be applied to robust, risk-seeking, and standard SOC problems. Thus, an approximate method based on extended Kalman filtering is developed and tested on the inverted pendulum problem, and compared with existing methods. As an application, the developed algorithm was adapted to a practically important problem in visual control in robotics known as image-based visual servoing (IBVS). The proposed control methodology for visual servoing was implemented for real-time experiments, and was compared with the standard IBVS methodology. The experimental results show that the proposed method can improve the myopic behaviors of standard IBVS methodology.
In this thesis, a new inference-based solution to stochastic optimal control (SOC) for general nonlinear systems is developed. This novel method applies to standard SOC problem, as well as robust and risk-seeking variations. The presented approach unifies many existing works, and makes possible, inference-based approximations to be applied to robust, risk-seeking, and standard SOC problems. Thus, an approximate method based on extended Kalman filtering is developed and tested on the inverted pendulum problem, and compared with existing methods. As an application, the developed algorithm was adapted to a practically important problem in visual control in robotics known as image-based visual servoing (IBVS). The proposed control methodology for visual servoing was implemented for real-time experiments, and was compared with the standard IBVS methodology. The experimental results show that the proposed method can improve the myopic behaviors of standard IBVS methodology.
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