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Edwin H.-m. Sha - One of the best experts on this subject based on the ideXlab platform.

  • Design Exploration With Imprecise Latency and Register Constraints
    IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2006
    Co-Authors: Chantana Chantrapornchai, W. Surakampontorn, Edwin H.-m. Sha
    Abstract:

    This paper proposes a Design Exploration framework that considers impreciseness in Design specification. In high-level synthesis, imprecise information is often encountered. Two types of impreciseness are considered, namely: 1) impreciseness underlying on functional unit specifications and 2) impreciseness due to system constraints, i.e., latency and register constraints. The framework is iterative and based on a core scheduling called "register-constrained inclusion scheduling." An example of how the scheduling algorithm works is shown. The effectiveness of the proposed framework for imprecise specification is demonstrated by exploring a Design solution for three well-known benchmarks, namely: 1) discrete cosine transform; 2) Voltera filter; and 3) fast Fourier transform. The selected solution meets the acceptability criteria while minimizing the total number of registers

  • ASIAN - Design Exploration framework under impreciseness based on register-constrained inclusion scheduling
    Advances in Computer Science - ASIAN 2004. Higher-Level Decision Making, 2004
    Co-Authors: Chantana Chantrapornchai, Wanlop Surakumpolthorn, Edwin H.-m. Sha
    Abstract:

    In this paper, we propose a Design Exploration framework which consider impreciseness in Design specification. In high-level synthesis, imprecise information is often encountered. Two kinds of impreciseness are considered here: imprecise characteristics of functional units and imprecise Design constraints. The proposed Design Exploration framework is based on efficient scheduling algorithm which considers impreciseness, Register-Constrained Inclusion Scheduling. We demonstrate the effectiveness of our framework by exploring a Design solution for a well-known benchmark, Voltera filter. The selected solution meets the acceptability criteria while minimizing the total number of registers.

  • EUC - Efficient scheduling for Design Exploration with imprecise latency and register constraints
    Embedded and Ubiquitous Computing, 2004
    Co-Authors: Chantana Chantrapornchai, Wanlop Surakumpolthorn, Edwin H.-m. Sha
    Abstract:

    In archiectural synthesis, scheduling and resource allocation are important steps. During the early stage of the Design, imprecise information is unavoidable. Under the imprecise system characteristics and constraints, this paper proposes a polynomial-time scheduling algorithm which minimizes both functional units and registers while scheduling. The algorithm can be used in Design Exploration for exploring the tradeoff between latency and register counts and selecting a solution with satisfactory performance and cost. The experiments show that we can achieve a schedule with the same acceptable degree while saving register upto 37% compared to the traditional algorithm.

  • Efficient Design Exploration based on module utility selection
    IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2000
    Co-Authors: Chantana Chantrapornchai, Edwin H.-m. Sha
    Abstract:

    In this paper, we present a Design Exploration framework, called WIZARD, which aims at finding module selections that will lead to superior Designs while considering scheduling and resource binding under latency and power constraints. The framework contains two phases: choosing the resource configuration, and determining a module binding for each resource. We introduce a powerful model called an acceptability function which models Design objectives, based on tradeoffs among different Design constraints as well as a user's willingness to accept a Design. Module utility measure cooperating with inclusion scheduling is the key to the success of our method. The utility of a module reflects the usefulness of the module based on the acceptability function. Inclusion scheduling is an algorithm to provide information for determining the number of functional units as well as module usefulness. We also present a heuristic which modifies module utility values based on the given acceptability function until they lead to superior selections. Many experiments on well-known benchmarks show the effectiveness of the approach when the obtained module selections are compared with the results from enumerating all module selections, as well as other schemes such as MSSR and PSGA.

Gianluca Iaccarino - One of the best experts on this subject based on the ideXlab platform.

  • Design Exploration and optimization under uncertainty
    Physics of Fluids, 2020
    Co-Authors: Aashwin Mishra, Jayant Mukhopadhaya, Juan J. Alonso, Gianluca Iaccarino
    Abstract:

    Computational strategies that explicitly quantify uncertainties are becoming increasingly used in aerospace applications to improve the consistency in reliability, robustness, and performance of Designs. A major source of uncertainty in simulations is due to the structural assumptions invoked in the formulation of turbulence models. Accounting for the turbulence model-form uncertainty has been described as “the greatest challenge” in simulation-based engineering Design. Despite its importance, Design Exploration and optimization under turbulence model-form uncertainty is an avenue that has not been investigated in depth in prior literature. In this investigation, we outline methodologies for the Design analysis, Exploration, and robust optimization under model-form uncertainty due to Reynolds averaged Navier–Stokes models. We exhibit how interval uncertainty estimates enable the use of alternative criteria for decision making under uncertainty in engineering Design. It is shown that such criteria can lead to different Design choices in Design Exploration. Finally, we carry out Design optimization under mixed uncertainties by using the perturbation framework in conjunction with polynomial chaos expansions. We introduce an approach for engineering Design optimization under uncertainty that utilizes physics-based uncertainty estimation along with decision theory criteria under uncertainty to produce Designs that are more robust to turbulence model uncertainties. These methodologies are illustrated via their application to complex turbulent flow cases, pertinent to aerospace Design applications.

Anoop Iyer - One of the best experts on this subject based on the ideXlab platform.

  • ICCAD - Application-driven processor Design Exploration for power-performance trade-off analysis
    IEEE ACM International Conference on Computer Aided Design. ICCAD 2001. IEEE ACM Digest of Technical Papers (Cat. No.01CH37281), 1
    Co-Authors: Diana Marculescu, Anoop Iyer
    Abstract:

    This paper presents an efficient Design Exploration environment for high-end core processors. The heart of the proposed Design Exploration framework is a two-level simulation engine that combines detailed simulation for critical portions of the code with fast profiling for the rest. Our two-level simulation methodology relies on the inherent clustered structure of application programs and is completely general and applicable to any microarchitectural power/performance simulation engine. The proposed simulation methodology is 3-17X faster, while being sufficiently accurate (within 5%) when compared to the fully detailed simulator. The Design Exploration environment is able to vary different microarchitectural configurations and find the optimal one as far as energyxdelay product is concerned in a matter of minutes. The parameters that are found to affect drastically the core processor power/performance metrics are issue width, instruction window size, and pipeline depth, along with correlated clock frequency. For very high-end configurations for which balanced pipelining may not be possible, opportunities for running faster stages at lower voltage exist. In such cases, by using up to 3 voltage levels, the energyxdelay product is reduced by 23-30% when compared to the single voltage implementation.

Frank M. Shipman - One of the best experts on this subject based on the ideXlab platform.

  • ASE - Combining the Analysis of Spatial Layout and Text to Support Design Exploration
    2008 23rd IEEE ACM International Conference on Automated Software Engineering, 2008
    Co-Authors: J.m. Moore, Frank M. Shipman
    Abstract:

    The Design Exploration (DE) approach allows a large number of probable end users to communicate with software developers by creating mockups of user interfaces and augmenting the partial Designs (e.g. windows and widgets) produced with textual descriptions and explanations. Software developers collect and analyze these annotated partial Designs to gain an understanding of the user, their activity, and the domain of concern. This paper presents the DE analyzer and its techniques for automatically analyzing the collection of annotated partial Designs. The DE Analyzer uses a combination of textual and spatial layout analysis algorithms to assist software developers' navigation and Exploration of the information collected using the DE Builder.

  • Design Exploration: engaging a larger user population
    2007
    Co-Authors: Frank M. Shipman, John Michael Moore
    Abstract:

    Software Designers must understand the domain, work practices, and user expectations before determining requirements or generating initial Design mock-ups. Users and other stakeholders are a valuable source of information leading to that understanding. Much work has focused on Design approaches that include users in the software development process. These approaches vary from surveys and questionnaires that garner responses from a population of potential users to participatory Design processes where representative users are included in the Design/development team. The Design Exploration approach retains the remote and asynchronous communication of surveys while making expression of feedback easier by providing users alternatives to textual communication for their suggestions and tacit understanding of the domain. To do this, visual and textual modes of expression are combined to facilitate communication from users to Designers while allowing a broad user audience to contribute to software Design. One challenge to such an approach is how software Designers make use of the potentially overwhelming combination of text, graphics, and other content. The Design Exploration process provides users and other stakeholders the Design Exploration Builder, a construction kit where they create annotated partial Designs. The Design Exploration Analyzer is an Exploration tool that allows software Designers to consolidate and explore partial Designs. The Analyzer looks for patterns based on textual analysis of annotations and spatial analysis of graphical Designs, to help identify interesting examples and patterns within the collection. Then software Designers can use this tool to search and browse within the Exploration set in order to better understand the task domain, user expectations and work practices. Evaluation of the tools has shown that users will often work to overcome expression constraints to convey information. Moreover, the mode of expression influences the types of information garnered. Furthermore, including more users results in greater coverage of the information gathered. These results provide evidence that Design Exploration is an approach that collects software and domain information from a large group of users that lies somewhere between questionnaires and face to face methods.* *This dissertation is a compound document (contains both a paper copy and a CD as part of the dissertation). The CD requires the following system requirements: Windows MediaPlayer or RealPlayer.

John L. Salmon - One of the best experts on this subject based on the ideXlab platform.

  • Multi-fidelity surrogates from shared principal components: Application to structural Design Exploration and optimization
    Structural and Multidisciplinary Optimization, 2021
    Co-Authors: Spencer Bunnell, Steven E. Gorrell, John L. Salmon
    Abstract:

    Computational cost of high-fidelity simulations limits the number of evaluations which may be performed in Design Exploration and optimization. Surrogates based on samples of multiple fidelities are used to decrease computational cost and lower error from single-fidelity surrogates. This paper develops a novel multi-fidelity surrogate model based on principal components which are shared between multiple fidelities of finite element model samples. This method does not require a common grid between the fidelities, further reducing computational cost. The new method was tested on various Design spaces of the Transonic Purdue Research Compressor and compared to other common and novel multi-fidelity methods. The new method was more accurate and required less computational cost than the other tested methods. Little to no increase in computational cost was needed to reduce surrogate error to 50% of the single-fidelity error. For fixed error, the computational cost was reduced by more than 75%. These results were also validated by testing the method on a more complex turbomachinery blade, Parametric Blade Study Rotor 4. The decreased error and computational cost improve effectiveness of Design Exploration and optimization. Such improvements help meet the demand for cleaner and safer engines by allowing high-fidelity Design Exploration within reasonable time frames.