Microarchitecture optimization for timing and layout

In recent years the drive to produce more complex integrated circuits while spending less design time has driven the demand for design automation tools. The search for design automation methods has resulted in the design of numerous behavioral synthesis and logic synthesis tools. This report describes a system that fills the gap between traditional behavioral synthesis and logic synthesis tools. Techniques are introduced for improving the microarchitecture structure and using feedback from lower-level optimization tools to guide design optimizations while attempting to meet user specified area and time constraints. These techniques include the capability for mixing layout styles such as custom layout for random-logic components and bit-slicing for regularly structured components. In this manner the entire design, control logic and datapath, can be optimized at the same time. Further, this paper presents a new methodology for microarchitecture-level optimization that greatly reduces the amount of technology-specific knowledge necessary to perform the optimizations

Multi-Objective Solution Based on Various Particle Swarm Optimization Techniques in Power Systems

A proposed optimization technique based on fuzzy logic and particle swarm is presented in this paper. This technique is referred to as Fuzzy Adaptive Particle Swarm Optimization (FAPSO). In this technique, the fuzzy logic is employed to adjust the parameters of the particle swarm. The proposed technique is applied to the IEEE-30-bus-system model along with previous optimization methods to obtain a multiobjective solution to the voltage control, the voltage deviation, and the real power loss problems in power systems. The multi-objective problem is subjected to the same constraints for all methods and a comparison between the results obtained by various methods is presented. It has been demonstrated that the results of the proposed technique superseded that of all previous optimization technique methods

Revisiting the Training of Logic Models of Protein Signaling Networks with a Formal Approach based on Answer Set Programming

A fundamental question in systems biology is the construction and training to data of mathematical models. Logic formalisms have become very popular to model signaling networks because their simplicity allows us to model large systems encompassing hundreds of proteins. An approach to train (Boolean) logic models to high-throughput phospho-proteomics data was recently introduced and solved using optimization heuristics based on stochastic methods. Here we demonstrate how this problem can be solved using Answer Set Programming (ASP), a declarative problem solving paradigm, in which a problem is encoded as a logical program such that its answer sets represent solutions to the problem. ASP has significant improvements over heuristic methods in terms of efficiency and scalability, it guarantees global optimality of solutions as well as provides a complete set of solutions. We illustrate the application of ASP with in silico cases based on realistic networks and data