Automated optimization of reconfigurable designs

Currently, the optimization of reconfigurable design parameters is typically done manually and often involves substantial amount effort. The main focus of this thesis is to reduce this effort. The designer can focus on the implementation and design correctness, leaving the tools to carry out optimization. To address this, this thesis makes three main contributions. First, we present initial investigation of reconfigurable design optimization with the Machine Learning Optimizer (MLO) algorithm. The algorithm is based on surrogate model technology and particle swarm optimization. By using surrogate models the long hardware generation time is mitigated and automatic optimization is possible. For the first time, to the best of our knowledge, we show how those models can both predict when hardware generation will fail and how well will the design perform. Second, we introduce a new algorithm called Automatic Reconfigurable Design Efficient Global Optimization (ARDEGO), which is based on the Efficient Global Optimization (EGO) algorithm. Compared to MLO, it supports parallelism and uses a simpler optimization loop. As the ARDEGO algorithm uses multiple optimization compute nodes, its optimization speed is greatly improved relative to MLO. Hardware generation time is random in nature, two similar configurations can take vastly different amount of time to generate making parallelization complicated. The novelty is efficient use of the optimization compute nodes achieved through extension of the asynchronous parallel EGO algorithm to constrained problems. Third, we show how results of design synthesis and benchmarking can be reused when a design is ported to a different platform or when its code is revised. This is achieved through the new Auto-Transfer algorithm. A methodology to make the best use of available synthesis and benchmarking results is a novel contribution to design automation of reconfigurable systems.Open Acces

A Brief Review on Mathematical Tools Applicable to Quantum Computing for Modelling and Optimization Problems in Engineering

Since its emergence, quantum computing has enabled a wide spectrum of new possibilities and advantages, including its efficiency in accelerating computational processes exponentially. This has directed much research towards completely novel ways of solving a wide variety of engineering problems, especially through describing quantum versions of many mathematical tools such as Fourier and Laplace transforms, differential equations, systems of linear equations, and optimization techniques, among others. Exploration and development in this direction will revolutionize the world of engineering. In this manuscript, we review the state of the art of these emerging techniques from the perspective of quantum computer development and performance optimization, with a focus on the most common mathematical tools that support engineering applications. This review focuses on the application of these mathematical tools to quantum computer development and performance improvement/optimization. It also identifies the challenges and limitations related to the exploitation of quantum computing and outlines the main opportunities for future contributions. This review aims at offering a valuable reference for researchers in fields of engineering that are likely to turn to quantum computing for solutions. Doi: 10.28991/ESJ-2023-07-01-020 Full Text: PD

Reliability constrained planning and sensitivity analysis for Solar-Wind-Battery based Isolated Power System

Isolated power systems have emerged as a practical substitute to grid extension for electrification of remote areas. The environmental hazards associated with conventional sources of energy like diesel and coal has forced system planners to resort to renewable energy sources(RES) based technologies such as solar and wind. Increased penetration of RES can effectively cut down system operating costs but can create reliability issues owing to their unpredictable nature. The risk of lower reliability standards can significantly hamper utilization of these sources on large scale. Thus an effective backup system is needed in order to ensure reliability standards. The backup is provided either by diesel generators or energy storage systems. The intermittent nature and cost intensive structure of RES based DGs makes it essential to perform sensitivity analyses for optimal system planning. In this paper, reliability and cost based sizing of solar-wind-battery storage system has been carried out for an Isolated hybrid power system(IHPS). Sensitivity analyses are performed by studying the effect of addition/removal of RES based DGs and storage units on system reliability. Considering variable nature of solar and wind sources, modelling of solar irradiance, wind speed and generator availability has been done using appropriate probability density functions. Dual reliability indices have been used for determining system reliability. For solving optimal sizing problem, a stochastic optimization technique Particle Swarm Optimization(PSO) has been employed. A new index termed as Incremental cost of reliability has been utilized in order to assess the additional investment required to improve reliability standards. Optimal sizing study in conjunction with sensitivity analyses facilitates a deeper insight into system planning