1,370 research outputs found
A Review of Bayesian Methods in Electronic Design Automation
The utilization of Bayesian methods has been widely acknowledged as a viable
solution for tackling various challenges in electronic integrated circuit (IC)
design under stochastic process variation, including circuit performance
modeling, yield/failure rate estimation, and circuit optimization. As the
post-Moore era brings about new technologies (such as silicon photonics and
quantum circuits), many of the associated issues there are similar to those
encountered in electronic IC design and can be addressed using Bayesian
methods. Motivated by this observation, we present a comprehensive review of
Bayesian methods in electronic design automation (EDA). By doing so, we hope to
equip researchers and designers with the ability to apply Bayesian methods in
solving stochastic problems in electronic circuits and beyond.Comment: 24 pages, a draft version. We welcome comments and feedback, which
can be sent to [email protected]
An Early History of Optimization Technology for Automated Design of Microwave Circuits
This paper outlines the early history of optimization technology for the design of microwave circuits—a personal journey filled with aspirations, academic contributions, and commercial innovations. Microwave engineers have evolved from being consumers of mathematical optimization algorithms to originators of exciting concepts and technologies that have spread far beyond the boundaries of microwaves. From the early days of simple direct search algorithms based on heuristic methods through gradient-based electromagnetic optimization to space mapping technology we arrive at today’s surrogate methodologies. Our path finally connects to today’s multi-physics, system-level, and measurement-based optimization challenges exploiting confined and feature-based surrogates, cognition-driven space mapping, Bayesian approaches, and more. Our story recognizes visionaries such as William J. Getsinger of the 1960s and Robert Pucel of the 1980s, and highlights a seminal decades-long collaboration with mathematician Kaj Madsen. We address not only academic contributions that provide proof of concept, but also indicate early formative milestones in the development of commercially competitive software specifically featuring optimization technology.ITESO, A.C
Post-Layout Simulation Driven Analog Circuit Sizing
Post-layout simulation provides accurate guidance for analog circuit design,
but post-layout performance is hard to be directly optimized at early design
stages. Prior work on analog circuit sizing often utilizes pre-layout
simulation results as the optimization objective. In this work, we propose a
post-layout-simulation-driven (post-simulation-driven for short) analog circuit
sizing framework that directly optimizes the post-layout simulation
performance. The framework integrates automated layout generation into the
optimization loop of transistor sizing and leverages a coupled Bayesian
optimization algorithm to search for the best post-simulation performance.
Experimental results demonstrate that our framework can achieve over 20% better
post-layout performance in competitive time than manual design and the method
that only considers pre-layout optimization
Advanced RF and Microwave Design Optimization: A Journey and a Vision of Future Trends
In this paper, we outline the historical evolution of RF and microwave design optimization and envisage imminent and future challenges that will be addressed by the next generation of optimization developments. Our journey starts in the 1960s, with the emergence of formal numerical optimization algorithms for circuit design. In our fast historical analysis, we emphasize the last two decades of documented microwave design optimization problems and solutions. From that retrospective, we identify a number of prominent scientific and engineering challenges: 1) the reliable and computationally efficient optimization of highly accurate system-level complex models subject to statistical uncertainty and varying operating or environmental conditions; 2) the computationally-efficient EM-driven multi-objective design optimization in high-dimensional design spaces including categorical, conditional, or combinatorial variables; and 3) the manufacturability assessment, statistical design, and yield optimization of high-frequency structures based on high-fidelity multi-physical representations. To address these major challenges, we venture into the development of sophisticated optimization approaches, exploiting confined and dimensionally reduced surrogate vehicles, automated feature-engineering-based optimization, and formal cognition-driven space mapping approaches, assisted by Bayesian and machine learning techniques.ITESO, A.C
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
On the Effects of Heterogeneous Errors on Multi-fidelity Bayesian Optimization
Bayesian optimization (BO) is a sequential optimization strategy that is
increasingly employed in a wide range of areas including materials design. In
real world applications, acquiring high-fidelity (HF) data through physical
experiments or HF simulations is the major cost component of BO. To alleviate
this bottleneck, multi-fidelity (MF) methods are used to forgo the sole
reliance on the expensive HF data and reduce the sampling costs by querying
inexpensive low-fidelity (LF) sources whose data are correlated with HF
samples. However, existing multi-fidelity BO (MFBO) methods operate under the
following two assumptions that rarely hold in practical applications: (1) LF
sources provide data that are well correlated with the HF data on a global
scale, and (2) a single random process can model the noise in the fused data.
These assumptions dramatically reduce the performance of MFBO when LF sources
are only locally correlated with the HF source or when the noise variance
varies across the data sources. In this paper, we dispense with these incorrect
assumptions by proposing an MF emulation method that (1) learns a noise model
for each data source, and (2) enables MFBO to leverage highly biased LF sources
which are only locally correlated with the HF source. We illustrate the
performance of our method through analytical examples and engineering problems
on materials design
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