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pipeline optimization for asynchronous circuits: Complexity analysis and an efficient optimal algorithm

IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 2006年第3期25卷 389-402页

作者： Kim, S Beerel, PA Synopsys Inc Hillsboro OR 97124 USA Univ So Calif Dept Elect Engn Syst Los Angeles CA 90089 USA

This paper addresses the problem of identifying the minimum pipelining needed in an asynchronous circuit (e.g., number/size of pipeline stages/latches required) to satisfy a given performance constraint, thereby implicitly minimizing area and power for a given performance. The paper first shows that the basic pipeline optimization problem for asynchronous circuits is NP-complete. Then, it presents an efficient branch and bound algorithm that finds the optimal pipeline configuration. The experimental results on a few scalable system models demonstrate that this algorithm is computationally feasible for moderately sized models.

关键词： asynchronous circuits complexity analysis pipeline optimization

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pipeline optimization for asynchronous circuits

Pipeline optimization for asynchronous circuits

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作者： Kim, Sangyun University of Southern California

学位级别：Ph.D.

This dissertation consists of two parts. The first part addresses computer-aided-design techniques to automate pipeline optimization of asynchronous circuits. The general problem is defined as identifying the minimal pipelining needed in an asynchronous circuit (e.g., number/size of pipeline stages/latches required) to satisfy a given performance constraint, thereby implicitly minimizing area and power for a given performance. In our work, we have proven that this problem is NP-complete and developed an efficient branch and bound solution that can find the optimal pipeline configuration for moderate sized deterministic pipelines models. The second part introduces structural reductions of probabilistic timed Petri nets that preserve a large class of performance metric. In particular, we propose a class of reductions that preserve efficiently computable bounds of statistics of time-separation of events (TSEs). It identifies two specific reductions within this class. It demonstrates the utility of these reductions by reducing a detailed Petri net describing the four-phase protocol of a well-known asynchronous pipeline template into a simpler two-phase architectural-level Petri net model. The benefit of this reduced model is that the run-time of subsequent TSE analysis can be greatly improved.

关键词： timing timed optimization problem choice instance lower bound slack cycle time styles Asynchronous pipelines asynchronous circuits optimization MODEL TSEs Handshaking pipeline optimization NCL

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Evaluation of a Tree-based pipeline optimization Tool for Automating Data Science 16

Evaluation of a Tree-based Pipeline Optimization Tool for Au...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Olson, Randal S. Bartley, Nathan Urbanowicz, Ryan J. Moore, Jason H. Univ Penn Philadelphia PA 19104 USA Univ Chicago Chicago IL 60637 USA

ISBN: (纸本)9781450342063

As the field of data science continues to grow, there will be an ever-increasing demand for tools that make machine learning accessible to non-experts. In this paper, we introduce the concept of tree-based pipeline optimization for automating one of the most tedious parts of machine learning-pipeline design. We implement an open source Tree-based pipeline optimization Tool (TPOT) in Python and demonstrate its effectiveness on a series of simulated and real-world benchmark data sets. In particular, we show that TPOT can design machine learning pipelines that provide a significant improvement over a basic machine learning analysis while requiring little to no input nor prior knowledge from the user. We also address the tendency for TPOT to design overly complex pipelines by integrating Pareto optimization, which produces compact pipelines without sacrificing classification accuracy. As such, this work represents an important step toward fully automating machine learning pipeline design.

关键词： pipeline optimization hyperparameter optimization data science machine learning genetic programming Pareto optimization Python

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Automating Biomedical Data Science Through Tree-Based pipeline optimization 19th

Automating Biomedical Data Science Through Tree-Based Pipeli...

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19th European Conference on the Applications of Evolutionary Computation (EvoApplications)

作者： Olson, Randal S. Urbanowicz, Ryan J. Andrews, Peter C. Lavender, Nicole A. Kidd, La Creis Moore, Jason H. Univ Penn Inst Biomed Informat 3700 Hamilton Walk Philadelphia PA 19104 USA Univ Louisville 505 S Hancock St Louisville KY 40202 USA

ISBN: (纸本)9783319312040

Over the past decade, data science and machine learning has grown from a mysterious art form to a staple tool across a variety of fields in academia, business, and government. In this paper, we introduce the concept of tree-based pipeline optimization for automating one of the most tedious parts of machine learning-pipeline design. We implement a Tree-based pipeline optimization Tool (TPOT) and demonstrate its effectiveness on a series of simulated and real-world genetic data sets. In particular, we show that TPOT can build machine learning pipelines that achieve competitive classification accuracy and discover novel pipeline operators-such as synthetic feature constructors-that significantly improve classification accuracy on these data sets. We also highlight the current challenges to pipeline optimization, such as the tendency to produce pipelines that overfit the data, and suggest future research paths to overcome these challenges. As such, this work represents an early step toward fully automating machine learning pipeline design.

关键词： pipeline optimization Hyperparameter optimization Data science Machine learning Genetic programming

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A Method of pipeline optimization for Reconfigurable Task 13

A Method of Pipeline Optimization for Reconfigurable Task

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13th IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Zhang, Jin Hu, Wei Shen, Huan Li, Yaxin Jia, Ruo Wuhan Univ Sci & Technol Coll Comp Sci & Technol Wuhan 430065 Hubei Peoples R China Hubei Prov Key Lab Intelligent Informat Proc & Re Wuhan Hubei Peoples R China

ISBN: (纸本)9781538637586

Reconfigurable computing is seen as an effective solution that combines the high flexibility of traditional processors with the high processing efficiency of ASIC. The delay caused by the GPC or RPU that is caused by reconfiguration in the reconfigurable system is called the configuration delay, and the configuration delay is purely performance overhead. Previous studies have proposed a number of different ways from the device layer to the application layer to reduce or hide the reconfiguration latency, but not a good solution to this problem. This paper realizes the pipeline of applications when they are reconstructed and configured on the FPGA platform, thus providing the efficiency of application execution and the flexibility of refactoring.

关键词： pipeline optimization FPGA Reorganizable Data stream

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CloudGT: A High Performance Genome Analysis Toolkit Leveraging pipeline optimization on Spark

CloudGT: A High Performance Genome Analysis Toolkit Leveragi...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM) - Human Genomics

作者： Xiao, Anghong Dong, Shoubin Liu, Cheng Zhang, Lingqi Wu, Zongze South China Univ Technol Sch Comp Sci & Engn Commun & Comp Network Lab Guangdong Wushan Rd 381 Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781538654880

The rapid development of NGS (Next Generation Sequencing) leads to the explosive increase of genome data. Massive data pose a great challenge to post-sequencing genomic data analysis process and put forward higher requirements for the performance and pipeline optimization of genome analysis tools. In this paper, we present CloudGT, a high performance genome analysis toolkit leveraging pipeline optimization based on Apache Spark. CloudGT accelerates genome analysis process by implementing parallelization of multi-core and multi-node using Spark framework. In order to improve the performance of genome analysis process, a pipeline optimization strategy based on Parquet storage is presented to reduce the storage space and I/O overhead throughout the process. Three main tools including CloudBWAMem, CloudDuplicateMark and CloudHaplotypeCaller to support read aligning, duplicate marking and variant calling are optimized and implemented which adapt to the pipeline optimization and further improve the overall performance. In our whole genome sequencing experiments, CloudGT is evaluated on a 10-node cluster and achieves high performance and scalability. The optimized pipeline totally reduces 80.95% storage space and 14.32% runtime than the unoptimized and is 26.6%-74.9% faster than state-of-the-art toolkits and approaches based on cluster with higher accuracy.

关键词： genome analysis pipeline Spark pipeline optimization

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Machine learning-based estimation of buildings' characteristics employing electrical and chilled water consumption data: pipeline optimization

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ENERGY AND BUILDINGS 2023年 295卷

作者： Raymand, Farhang Najafi, Behzad Mamaghani, Alireza Haghighat Moazami, Amin Rinaldi, Fabio Radboud Univ Nijmegen Dept Environm Sci Heyendaalseweg 135 NL-6525 AJ Nijmegen Netherlands Politecn Milan Dipartimento Energia Via Lambruschini 4 I-20156 Milan Italy Concordia Univ Dept Bldg Civil & Environm Engn 1455 Maisonneuve Blvd W BE 351 Montreal PQ H3G 1M8 Canada NTNU Fac Engn Dept Ocean Operat & Civil Engn Alesund Norway SINTEF AS Dept Architectural Engn SINTEF Community Borrestuveien 3 N-0373 Oslo Norway

Smart meter-driven remote auditing of buildings, as an alternative to the labor-intensive on-site visits, permits large-scale and rapid identification of buildings with low energy performance. The existing literature has mainly focused on electricity meters' data from a rather small set of buildings and efforts have often not been made to facilitate the models' physical interpretability. Accordingly, the present work focuses on the implementation and optimization of ML-based pipelines for building characterization (by use type (A), performance class (B), and operation group (C)) employing hourly electrical and chilled-water consumption data. Utilizing the Building Data Genome Project II dataset (with data from 1636 buildings), feature generation, feature selection, and pipeline optimization steps are performed for each pipeline. Results demonstrate that performing the latter two steps improves the model's accuracy (5.3%, 2.9%, and 3.9% for pipelines A, B, and C compared to a benchmark model), while notably reduces the number of utilized features (94.7%, 88.3%, 89.4%), enhancing the models' interpretability. Furthermore, adding features extracted from chilled-water consumption data boosts the accuracy (with respect to baseline) for the second subset by 12.4%, 13.5%, and 7.2%, while decreasing the feature count by 97.2%, 96.4%, and 96.5%, respectively.

关键词： Smart meter Commercial buildings classification Machine learning Feature extraction Feature selection pipeline optimization

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Research on low carbon emission optimization operation technology of natural gas pipeline under multi-energy structure

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Petroleum Science 2022年第6期19卷 3046-3058页

作者： En-Bin Liu Yong Peng Shan-Bi Peng Bin Yu Qi-Kun Chen Petroleum Engineering School Southwest Petroleum UniversityChengdu610500China School of Civil Engineering and Geomatics Southwest Petroleum UniversityChengdu610500China China Petroleum Pipeline Engineering Corporation Hebei065000China School of Engineering Cardiff UniversityCardiffCF243AAUK

In order to reduce the carbon emissions of natural gas pipelines,based on the background of different energy structures,this paper proposes a general low carbon and low consumption operation model of natural gas pipelines,which is used to fine calculate the carbon emissions and energy consumption of natural gas *** this paper,an improved particle swarm optimization(NHPSO-JTVAC)algorithm is used to solve the model and the optimal scheduling scheme is *** a parallel pipeline located in western China as an example,the case is *** results show that after optimization,under the existing energy types,the pipeline system can reduce 31.14%of carbon emissions,and after introducing part of new energy,the pipeline system can reduce 34.02%of carbon emissions,but the energy consumption has increased.

关键词： Natural gas pipeline optimization NHPSO-JTVAC algorithm Carbon emission New energy

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MPEC strategies for cost optimization of pipeline operations

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COMPUTERS & CHEMICAL ENGINEERING 2010年第6期34卷 900-913页

作者： Baumrucker, B. T. Biegler, L. T. Carnegie Mellon Univ Dept Chem Engn Pittsburgh PA 15213 USA

This study develops a mathematical program with equilibrium constraints (MPECs) approach for efficient operation of gas pipelines. The resulting model handles time dependent operations in order to determine minimum energy consumption and operating cost over a given time horizon. The MPEC structure also allows flow reversals, flow transitions and other nonsmooth elements to be incorporated within the approach. Applied to industrial gas pipelines, this approach can also deal with customer demand satisfaction in the presence of compressor outages and minimize recovery time for systems that are unable to meet customer demands at all times. A large-scale oxygen pipeline case study is considered to demonstrate this approach and complex energy pricing schemes are also applied to this problem. These schemes include time of day electricity pricing, along with extensions to Real Time Pricing and Day Ahead Pricing. Compared to flat rate and minimum energy optimizations, respectively, we observe operating cost savings up to 5.13% for time of day electricity pricing and up to 12.85% for Real Time Pricing. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： pipeline optimization Electricity pricing Dynamic optimization Complementarity MPEC Nonlinear programming

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Lifecycle cost optimization of pipeline projects

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JOURNAL OF WATER SUPPLY RESEARCH AND TECHNOLOGY-AQUA 2020年第7期69卷 656-667页

作者： Al-Khomairi, Abdulrahman Jung, BongSeog Elsebaie, Ibrahim King Saud Univ Civil Engn Dept Coll Engn Riyadh Saudi Arabia HydraTek & Associates Vaughan ON Canada

Lifecycle cost optimization for a pipeline network with medium-sized pipes is performed considering steady and unsteady flow conditions. Genetic algorithms are used to generate a wide range of hydraulically acceptable solutions and search for the most economical solutions. The impact of each cost component on the total cost is determined in this study. The decision variables include the pipe diameter, pipe material, pipe pressure rating, surge tank size and operational and maintenance costs over the project service life. A real-case project is presented to crosscheck the suggested procedure. Significant cost variations are observed, even between equally acceptable designs. Furthermore, the operational cost has a deterministic effect on the parameters of the optimum solution. Compared to conventional design wisdom that focuses on reducing the pipe diameter as much as possible to reduce the project cost, this approach demonstrates that significant savings in pipeline project costs can be achieved by carefully investigating all possible design alternatives under steady and unsteady flow conditions.

关键词： global optimization lifecyle cost analysis lifecyle cost optimization pipeline cost estimation pipeline optimization pipeline projects

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