Faster Generation of Feasible Design Points

Design space exploration during conceptual design is an active research field. Most approaches generate a number of feasible design points (complying with the constraints) and apply graphical post-processing to visualize correlations between variables, the Pareto frontier or a preference structure among the design solutions. The generation of feasible design points is often a statistical (Monte Carlo) generation of potential candidates sampled within initial variable domains, followed by a verification of constraint satisfaction, which may become inefficient if the design problem is highly constrained since a majority of candidates that are generated do not belong to the (small) feasible solution space. In this paper, we propose to perform a preliminary analysis with Constraint Programming techniques that are based on interval arithmetic to dramatically prune the solution space before using statistical (Monte Carlo) methods to generate candidates in the design space. This method requires that the constraints are expressed in an analytical form. A case study involving truss design under uncertainty is presented to demonstrate that the computation time for generating a given number of feasible design points is greatly improved using the proposed method. The integration of both techniques provides a flexible mechanism to take successive design refinements into account within a dynamic process of design under uncertainty.

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Monte Carlo Design Space Exploration of Superconducting Wind Generator Using MgB2 and YBCO Conductors

IEEE Transactions on Applied Superconductivity ◽

10.1109/tasc.2016.2524563 ◽

2016 ◽

Vol 26 (3) ◽

pp. 1-5 ◽

Cited By ~ 3

Author(s):

Salvatore Grieco ◽

Yaw D. Nyanteh ◽

Philippe J. Masson

Keyword(s):

Monte Carlo ◽

Design Space Exploration ◽

Design Space ◽

Space Exploration ◽

Wind Generator

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Design Space Exploration for YOLO Neural Network Accelerator

Electronics ◽

10.3390/electronics9111921 ◽

2020 ◽

Vol 9 (11) ◽

pp. 1921

Author(s):

Hongmin Huang ◽

Zihao Liu ◽

Taosheng Chen ◽

Xianghong Hu ◽

Qiming Zhang ◽

...

Keyword(s):

Neural Network ◽

Design Space Exploration ◽

Design Space ◽

Space Exploration ◽

Computation Time ◽

Memory Bandwidth ◽

Block Transmission ◽

Transmission Strategy ◽

The Neural Network ◽

The Given

The You Only Look Once (YOLO) neural network has great advantages and extensive applications in computer vision. The convolutional layers are the most important part of the neural network and take up most of the computation time. Improving the efficiency of the convolution operations can greatly increase the speed of the neural network. Field programmable gate arrays (FPGAs) have been widely used in accelerators for convolutional neural networks (CNNs) thanks to their configurability and parallel computing. This paper proposes a design space exploration for the YOLO neural network based on FPGA. A data block transmission strategy is proposed and a multiply and accumulate (MAC) design, which consists of two 14 × 14 processing element (PE) matrices, is designed. The PE matrices are configurable for different CNNs according to the given required functions. In order to take full advantage of the limited logical resources and the memory bandwidth on the given FPGA device and to simultaneously achieve the best performance, an improved roofline model is used to evaluate the hardware design to balance the computing throughput and the memory bandwidth requirement. The accelerator achieves 41.99 giga operations per second (GOPS) and consumes 7.50 W running at the frequency of 100 MHz on the Xilinx ZC706 board.

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P3GA: An Algorithm for Technology Characterization

Journal of Mechanical Design ◽

10.1115/1.4028101 ◽

2015 ◽

Vol 137 (1) ◽

Cited By ~ 8

Author(s):

Edgar Galvan ◽

Richard J. Malak

Keyword(s):

Design Automation ◽

Design Space Exploration ◽

Design Space ◽

Space Exploration ◽

Numerical Test ◽

Pareto Frontier ◽

Test Problems ◽

Genetic Optimization ◽

Technology Evaluation ◽

Mathematical Basis

It is important for engineers to understand the capabilities and limitations of the technologies they consider for use in their systems. However, communicating this information can be a challenge. Mathematical characterizations of technical capabilities are of interest as a means to reduce ambiguity in communication and to increase opportunities to utilize design automation methods. The parameterized Pareto frontier (PPF) was introduced in prior work as a mathematical basis for modeling technical capabilities. One advantage of PPFs is that, in many cases, engineers can model a system by composing frontiers of its components. This allows for rapid technology evaluation and design space exploration. However, finding the PPF can be difficult. The contribution of this article is a new algorithm for approximating the PPF, called predictive parameterized Pareto genetic algorithm (P3GA). The proposed algorithm uses concepts and methods from multi-objective genetic optimization and machine learning to generate a discrete approximation of the PPF. If needed, designers can generate a continuous approximation of the frontier by generalizing beyond these data. The algorithm is explained, its performance is analyzed on numerical test problems, and its use is demonstrated on an engineering example. The results of the investigation indicate that P3GA may be effective in practice.

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Effects of Robust Convex Optimization on Early-Stage Design Space Exploratory Behavior

Journal of Mechanical Design ◽

10.1115/1.4048580 ◽

2020 ◽

Vol 142 (12) ◽

Author(s):

Priya P. Pillai ◽

Edward Burnell ◽

Xiqing Wang ◽

Maria C. Yang

Keyword(s):

Robust Optimization ◽

Design Space Exploration ◽

Design Space ◽

Early Stage ◽

Pareto Frontier ◽

Design Parameters ◽

Safety Factors ◽

Worst Case ◽

Robust Convex Optimization ◽

Early Stage Design

Abstract Engineers design for an inherently uncertain world. In the early stages of design processes, they commonly account for such uncertainty either by manually choosing a specific worst-case and multiplying uncertain parameters with safety factors or by using Monte Carlo simulations to estimate the probabilistic boundaries in which their design is feasible. The safety factors of this first practice are determined by industry and organizational standards, providing a limited account of uncertainty; the second practice is time intensive, requiring the development of separate testing infrastructure. In theory, robust optimization provides an alternative, allowing set-based conceptualizations of uncertainty to be represented during model development as optimizable design parameters. How these theoretical benefits translate to design practice has not previously been studied. In this work, we analyzed the present use of geometric programs as design models in the aerospace industry to determine the current state-of-the-art, then conducted a human-subjects experiment to investigate how various mathematical representations of uncertainty affect design space exploration. We found that robust optimization led to far more efficient explorations of possible designs with only small differences in an experimental participant’s understanding of their model. Specifically, the Pareto frontier of a typical participant using robust optimization left less performance “on the table” across various levels of risk than the very best frontiers of participants using industry-standard practices.

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Multi-Objective Composite Panel Optimization Using Machine Learning Classifiers and Genetic Algorithms

Volume 2A: 42nd Design Automation Conference ◽

10.1115/detc2016-60125 ◽

2016 ◽

Author(s):

Kayla Zeliff ◽

Walter Bennette ◽

Scott Ferguson

Keyword(s):

Design Space Exploration ◽

Design Space ◽

Pareto Frontier ◽

Composite Panel ◽

Ensemble Classifiers ◽

Solution Quality ◽

Machine Learning Classifiers ◽

Multi Objective ◽

Multi Objective Genetic Algorithm ◽

Statistical Rule

Design spaces that consist of millions or billions of design combinations pose a challenge to current methods for identifying optimal solutions. Complex analyses can also lead to lengthy computation times that further challenge the effectiveness of an algorithm in terms of solution quality and run-time. This work explores combining the design space exploration approach of a Multi-Objective Genetic Algorithm with different instance-based, statistical, rule-based and ensemble classifiers to reduce the number of unnecessary function evaluations associated with poorly performing designs. Results indicate that introducing a classifier to identify child designs that are likely to push the Pareto frontier toward an optima reduce the number of function calculations by 75–85%, depending on the classifier implemented.

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Use Scenarios for Design Space Exploration With a Dynamic Multiobjective Optimization Formulation

Volume 3: 38th Design Automation Conference, Parts A and B ◽

10.1115/detc2012-71039 ◽

2012 ◽

Author(s):

Shane K. Curtis ◽

Braden J. Hancock ◽

Christopher A. Mattson

Keyword(s):

Multiobjective Optimization ◽

Conceptual Design ◽

Design Space Exploration ◽

Optimization Problem ◽

Design Space ◽

Optimization Problems ◽

Space Exploration ◽

Pareto Frontier ◽

Problem Formulation ◽

Design And Optimization

In a recent publication, we presented a new strategy for engineering design and optimization, which we termed formulation space exploration. The formulation space for an optimization problem is the union of all variable and design objective spaces identified by the designer as being valid and pragmatic problem formulations. By extending a computational search into this new space, the solution to any optimization problem is no longer predefined by the optimization problem formulation. This method allows a designer to both diverge the design space during conceptual design and converge onto a solution as more information about the design objectives and constraints becomes available. Additionally, we introduced a new way to formulate multiobjective optimization problems, allowing the designer to change and update design objectives, constraints, and variables in a simple, fluid manner that promotes exploration. In this paper, we investigate three use scenarios where formulation space exploration can be utilized in the early stages of design when it is possible to make the greatest contributions to development projects. Specifically, we look at s-Pareto frontier generation in the formulation space, formulation space boundary exploration, and a new way to perform inverse optimization. The benefits of these methods are illustrated with the conceptual design of an impact driver.

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