Relevant bibliographies by topics / Energy Efficient Machine Learning

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Energy Efficient Machine Learning'

Author: Grafiati
Published: 4 June 2021
Last updated: 15 February 2022

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Journal articles on the topic "Energy Efficient Machine Learning"

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Masikos, Michail, Michael Theologou, Konstantinos Demestichas, and Evgenia Adamopoulou. "Machine-learning methodology for energy efficient routing." IET Intelligent Transport Systems 8, no. 3 (2014): 255–65. http://dx.doi.org/10.1049/iet-its.2013.0006.

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Shanthi, D., and Ashwani Kumar. "Energy Efficient NoC design through Supervised Machine Learning." Journal of Physics: Conference Series 1998, no. 1 (2021): 012002. http://dx.doi.org/10.1088/1742-6596/1998/1/012002.

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Zhang, Huanhuan, Jigeng Li, and Mengna Hong. "Machine Learning-Based Energy System Model for Tissue Paper Machines." Processes 9, no. 4 (2021): 655. http://dx.doi.org/10.3390/pr9040655.

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With the global energy crisis and environmental pollution intensifying, tissue papermaking enterprises urgently need to save energy. The energy consumption model is essential for the energy saving of tissue paper machines. The energy consumption of tissue paper machine is very complicated, and the workload and difficulty of using the mechanism model to establish the energy consumption model of tissue paper machine are very large. Therefore, this article aims to build an empirical energy consumption model for tissue paper machines. The energy consumption of this model includes electricity consu
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Oh, Myeung Suk, Gibum Kim, and Hyuncheol Park. "Machine-Learning-Based Link Adaptation for Energy-Efficient MIMO-OFDM Systems." Journal of Korean Institute of Electromagnetic Engineering and Science 27, no. 5 (2016): 407–15. http://dx.doi.org/10.5515/kjkiees.2016.27.5.407.

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Moayedi, Hossein, Dieu Tien Bui, Anastasios Dounis, Zongjie Lyu, and Loke Kok Foong. "Predicting Heating Load in Energy-Efficient Buildings Through Machine Learning Techniques." Applied Sciences 9, no. 20 (2019): 4338. http://dx.doi.org/10.3390/app9204338.

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The heating load calculation is the first step of the iterative heating, ventilation, and air conditioning (HVAC) design procedure. In this study, we employed six machine learning techniques, namely multi-layer perceptron regressor (MLPr), lazy locally weighted learning (LLWL), alternating model tree (AMT), random forest (RF), ElasticNet (ENet), and radial basis function regression (RBFr) for the problem of designing energy-efficient buildings. After that, these approaches were used to specify a relationship among the parameters of input and output in terms of the energy performance of buildin
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Abebe, Misganaw, Yongwoo Shin, Yoojeong Noh, Sangbong Lee, and Inwon Lee. "Machine Learning Approaches for Ship Speed Prediction towards Energy Efficient Shipping." Applied Sciences 10, no. 7 (2020): 2325. http://dx.doi.org/10.3390/app10072325.

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As oil prices continue to rise internationally, shipping costs are also increasing rapidly. In order to reduce fuel costs, an economical shipping route must be determined by accurately predicting the estimated arrival time of ships. A common method in the evaluation of ship speed involves computing the total resistance of a ship using theoretical analysis; however, using theoretical equations cannot be applied for most ships under various operating conditions. In this study, a machine learning approach was proposed to predict ship speed over the ground using the automatic identification system
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Ilager, Shashikant, Kotagiri Ramamohanarao, and Rajkumar Buyya. "Thermal Prediction for Efficient Energy Management of Clouds Using Machine Learning." IEEE Transactions on Parallel and Distributed Systems 32, no. 5 (2021): 1044–56. http://dx.doi.org/10.1109/tpds.2020.3040800.

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Plazinski, Wojciech, Anita Plazinska, and Agnieszka Brzyska. "Efficient sampling of high-energy states by machine learning force fields." Physical Chemistry Chemical Physics 22, no. 25 (2020): 14364–74. http://dx.doi.org/10.1039/d0cp01399d.

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Kang, Mingu, Prakalp Srivastava, Vikram Adve, Nam Sung Kim, and Naresh R. Shanbhag. "An Energy-Efficient Programmable Mixed-Signal Accelerator for Machine Learning Algorithms." IEEE Micro 39, no. 5 (2019): 64–72. http://dx.doi.org/10.1109/mm.2019.2929502.

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Clay, James, Naveena Elango, Sheena Ratnam Priya, Shixiong Jiang, and Ramalingam Sridhar. "Energy-efficient and reliable in-memory classifier for machine-learning applications." IET Computers & Digital Techniques 13, no. 6 (2019): 443–52. http://dx.doi.org/10.1049/iet-cdt.2019.0040.

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Dissertations / Theses on the topic "Energy Efficient Machine Learning"

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Azmat, Freeha. "Machine learning and energy efficient cognitive radio." Thesis, University of Warwick, 2016. http://wrap.warwick.ac.uk/85990/.

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With an explosion of wireless mobile devices and services, system designers are facing a challenge of spectrum scarcity and high energy consumption. Cognitive radio (CR) is a promising solution for fulfilling the growing demand of radio spectrum using dynamic spectrum access. It has the ability of sensing, allocating, sharing and adapting to the radio environment. In this thesis, an analytical performance evaluation of the machine learning and energy efficient cognitive radio systems has been investigated while taking some realistic conditions into account. Firstly, bio-inspired techniques, in
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García-Martín, Eva. "Extraction and Energy Efficient Processing of Streaming Data." Licentiate thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-15532.

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The interest in machine learning algorithms is increasing, in parallel with the advancements in hardware and software required to mine large-scale datasets. Machine learning algorithms account for a significant amount of energy consumed in data centers, which impacts the global energy consumption. However, machine learning algorithms are optimized towards predictive performance and scalability. Algorithms with low energy consumption are necessary for embedded systems and other resource constrained devices; and desirable for platforms that require many computations, such as data centers. Data s
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Le, Borgne Yann-Aël. "Learning in wireless sensor networks for energy-efficient environmental monitoring." Doctoral thesis, Universite Libre de Bruxelles, 2009. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210334.

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Wireless sensor networks form an emerging class of computing devices capable of observing the world with an unprecedented resolution, and promise to provide a revolutionary instrument for environmental monitoring. Such a network is composed of a collection of battery-operated wireless sensors, or sensor nodes, each of which is equipped with sensing, processing and wireless communication capabilities. Thanks to advances in microelectronics and wireless technologies, wireless sensors are small in size, and can be deployed at low cost over different kinds of environments in order to monitor both
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Yurur, Ozgur. "Energy Efficient Context-Aware Framework in Mobile Sensing." Scholar Commons, 2013. http://scholarcommons.usf.edu/etd/4797.

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The ever-increasing technological advances in embedded systems engineering, together with the proliferation of small-size sensor design and deployment, have enabled mobile devices (e.g., smartphones) to recognize daily occurring human based actions, activities and interactions. Therefore, inferring a vast variety of mobile device user based activities from a very diverse context obtained by a series of sensory observations has drawn much interest in the research area of ubiquitous sensing. The existence and awareness of the context provides the capability of being conscious of physical environ
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Zayene, Mariem. "Cooperative data exchange for wireless networks : Delay-aware and energy-efficient approaches." Thesis, Limoges, 2019. http://www.theses.fr/2019LIMO0033/document.

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Avec le nombre croissant d’appareils intelligents à faible puissance, au cours ces dernières années, la question de l’efficacité énergétique a joué un rôle de plus en plus indispensable dans la conception des systèmes de communication. Cette thèse vise à concevoir des schémas de transmission distribués à faible consommation d’énergie pour les réseaux sans fil, utilisant la théorie des jeux et le codage réseau instantanément décodable (IDNC), qui est une sous-classe prometteuse du codage réseau. En outre, nous étudions le modèle de l'échange coopératif de donnée (CDE) dans lequel tous les périp
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Kheffache, Mansour. "Energy-Efficient Detection of Atrial Fibrillation in the Context of Resource-Restrained Devices." Thesis, Luleå tekniska universitet, Datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-76394.

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eHealth is a recently emerging practice at the intersection between the ICT and healthcare fields where computing and communication technology is used to improve the traditional healthcare processes or create new opportunities to provide better health services, and eHealth can be considered under the umbrella of the Internet of Things. A common practice in eHealth is the use of machine learning for a computer-aided diagnosis, where an algorithm would be fed some biomedical signal to provide a diagnosis, in the same way a trained radiologist would do. This work considers the task of Atrial Fibr
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Zorello, Ligia Maria Moreira. "Dynamic CPU frequency scaling using machine learning for NFV applications." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/3/3141/tde-30012019-100044/.

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Growth in the Information and Communication Technology sector is increasing the need to improve the quality of service and energy efficiency, as this industry has already surpassed 12% of global energy consumption in 2017. Data centers correspond to a large part of this consumption, accounting for about 15% of energy expenditure on the Information and Communication Technology domain; moreover, the subsystem that generates the most costs for data center operators is that of servers and storage. Many solutions have been proposed to reduce server consumption, such as the use of dynamic voltage an
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Guss, Herman, and Linus Rustas. "Applying Machine Learning Algorithms for Anomaly Detection in Electricity Data : Improving the Energy Efficiency of Residential Buildings." Thesis, Uppsala universitet, Byggteknik och byggd miljö, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-415507.

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The purpose of this thesis is to investigate how data from a residential property owner can be utilized to enable better energy management for their building stock. Specifically, this is done through the development of two machine learning models with the objective of detecting anomalies in the existing data of electricity consumption. The dataset consists of two years of residential electricity consumption for 193 substations belonging to the residential property owner Uppsalahem. The first of the developed models uses the K-means method to cluster substations with similar consumption pattern
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Seeker, Volker Günter. "User experience driven CPU frequency scaling on mobile devices : towards better energy efficiency." Thesis, University of Edinburgh, 2017. http://hdl.handle.net/1842/29583.

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With the development of modern smartphones, mobile devices have become ubiquitous in our daily lives. With high processing capabilities and a vast number of applications, users now need them for both business and personal tasks. Unfortunately, battery technology did not scale with the same speed as computational power. Hence, modern smartphone batteries often last for less than a day before they need to be recharged. One of the most power hungry components is the central processing unit (CPU). Multiple techniques are applied to reduce CPU energy consumption. Among them is dynamic voltage and f
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Collard, Sophie. "Assessing and Predicting the Impact of Energy Conservation Measures Using Smart Meter Data." Thesis, KTH, Kraft- och värmeteknologi, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-150352.

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Buildings account for around 40 percent of the primary energy consumption in Europe and in the United States. They also hold tremendous energy savings potential: 15 to 29 percent by 2020 for the European building stock according to a 2009 study from the European Commission. Verifying and predicting the impact of energy conservation measures in buildings is typically done through energy audits. These audits are costly, time-consuming, and may have high error margins if only limited amounts of data can be collected. The ongoing large-scale roll-out of smart meters and wireless sensor networks in
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Books on the topic "Energy Efficient Machine Learning"

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Zein, André. Transition Towards Energy Efficient Machine Tools. Springer Berlin Heidelberg, 2012.

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Zein, André. Transition Towards Energy Efficient Machine Tools. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32247-1.

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Ertekin, Şeyda. Algorithms for efficient learning systems: Online and active learning approaches. VDM Verlag Dr. Müller, 2009.

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The design and analysis of efficient learning algorithms. MIT Press, 1992.

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Approximation methods for efficient learning of Bayesian networks. IOS Press, 2008.

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Aronsson, Stefan. Learning from experiences with energy efficient lighting in commercial buildings. Centre for the Analysis and Dissemination of Demonstrated Energy Technologies, CADDET Analysis Support Unit, 1991.

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Magoulès, Frédéric, and Hai-Xiang Zhao. Data Mining and Machine Learning in Building Energy Analysis. John Wiley & Sons, Inc., 2016. http://dx.doi.org/10.1002/9781118577691.

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Mathur, Puneet. IoT Machine Learning Applications in Telecom, Energy, and Agriculture. Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-5549-0.

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Tomar, Anuradha, Hasmat Malik, Pramod Kumar, and Atif Iqbal, eds. Machine Learning, Advances in Computing, Renewable Energy and Communication. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-2354-7.

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Pathak, Manas A. Privacy-Preserving Machine Learning for Speech Processing. Springer New York, 2013.

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Book chapters on the topic "Energy Efficient Machine Learning"

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Berral, Josep Ll, Iñigo Goiri, Ramon Nou, et al. "Toward Energy-Aware Scheduling Using Machine Learning." In Energy-Efficient Distributed Computing Systems. John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118342015.ch8.

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Hanif, Muhammad Abdullah, Rehan Hafiz, Muhammad Usama Javed, Semeen Rehman, and Muhammad Shafique. "Energy-Efficient Design of Advanced Machine Learning Hardware." In Machine Learning in VLSI Computer-Aided Design. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-04666-8_21.

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Zhang, Tong, Gang Wang, Ruofei Zhou, Yikun Zou, and Mingchuan Yang. "Energy Efficient Communication of Fuel-Powered UAV Relay, Design of Positions and Power Allocation." In Machine Learning and Intelligent Communications. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66785-6_52.

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Reddy, V. Dinesh, G. R. Gangadharan, G. S. V. R. K. Rao, and Marco Aiello. "Energy-Efficient Resource Allocation in Data Centers Using a Hybrid Evolutionary Algorithm." In Machine Learning for Intelligent Decision Science. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3689-2_4.

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Liu, Xin, Xueyan Zhang, Weidang Lu, and Mudi Xiong. "Energy Efficiency Maximization for Green Cognitive Internet of Things with Energy Harvesting." In Machine Learning and Intelligent Communications. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32388-2_24.

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Wang, Xi, Xiangbin Yu, Tao Teng, and Guangying Wang. "Energy-Efficient Power Allocation Scheme Based on Discrete-Rate Adaptive Modulation in Distributed Antenna System." In Machine Learning and Intelligent Communications. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00557-3_29.

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Bhatt, Varun, and Udayan Ganguly. "Sparsity Enables Data and Energy Efficient Spiking Convolutional Neural Networks." In Artificial Neural Networks and Machine Learning – ICANN 2018. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01418-6_26.

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Pal, R. K., Parveen Goyal, and Shankar Sehgal. "Thermal Performance of Natural Insulation Materials for Energy Efficient Buildings." In Artificial Intelligence, Machine Learning, and Data Science Technologies. CRC Press, 2021. http://dx.doi.org/10.1201/9781003153405-9.

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Kachris, Christoforos, Elias Koromilas, Ioannis Stamelos, Georgios Zervakis, Sotirios Xydis, and Dimitrios Soudris. "Energy-Efficient Acceleration of Spark Machine Learning Applications on FPGAs." In Hardware Accelerators in Data Centers. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-92792-3_5.

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Nafis, Md Tabrez, Aksa Urooj, and Siddhartha Sankar Biswas. "Recent Machine Learning and Internet of Things (IoT) Applications for Personalized Healthcare: Issues and Challenges." In Sustainable and Energy Efficient Computing Paradigms for Society. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-51070-1_7.

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Conference papers on the topic "Energy Efficient Machine Learning"

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Kumar, Mohit, Xingzhou Zhang, Liangkai Liu, Yifan Wang, and Weisong Shi. "Energy-Efficient Machine Learning on the Edges." In 2020 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). IEEE, 2020. http://dx.doi.org/10.1109/ipdpsw50202.2020.00153.

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Curtis-Maury, Matthew, Karan Singh, Sally A. McKee, et al. "Identifying energy-efficient concurrency levels using machine learning." In 2007 IEEE International Conference on Cluster Computing (CLUSTER). IEEE, 2007. http://dx.doi.org/10.1109/clustr.2007.4629274.

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Lata, Sonam, and Shabana Mehfuz. "Machine Learning based Energy Efficient Wireless Sensor Network." In 2019 International Conference on Power Electronics, Control and Automation (ICPECA). IEEE, 2019. http://dx.doi.org/10.1109/icpeca47973.2019.8975526.

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Kim, Hyunjoo, Annette Stumpf, and Richard Schneider. "Developing Energy Efficient Building Design in Machine Learning." In 27th International Symposium on Automation and Robotics in Construction. International Association for Automation and Robotics in Construction (IAARC), 2010. http://dx.doi.org/10.22260/isarc2010/0053.

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Venkataramani, Swagath, Anand Raghunathan, Jie Liu, and Mohammed Shoaib. "Scalable-effort classifiers for energy-efficient machine learning." In DAC '15: The 52nd Annual Design Automation Conference 2015. ACM, 2015. http://dx.doi.org/10.1145/2744769.2744904.

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Hao Yu. "Energy efficient VLSI circuits for machine learning on-chip." In 2017 International Symposium on VLSI Design, Automation and Test (VLSI-DAT). IEEE, 2017. http://dx.doi.org/10.1109/vlsi-dat.2017.7939671.

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Mao, Wei, Zhihua Xiao, Peng Xu, et al. "Energy-Efficient Machine Learning Accelerator for Binary Neural Networks." In GLSVLSI '20: Great Lakes Symposium on VLSI 2020. ACM, 2020. http://dx.doi.org/10.1145/3386263.3407582.

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Imes, Connor, Steven Hofmeyr, and Henry Hoffmann. "Energy-efficient Application Resource Scheduling using Machine Learning Classifiers." In ICPP 2018: 47th International Conference on Parallel Processing. ACM, 2018. http://dx.doi.org/10.1145/3225058.3225088.

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Ma, Tian-yi, Zhi-qiang Li, and Jun Yang. "A Novel Neural Network Search for Energy-Efficient Hardware-Software Partitioning." In 2006 International Conference on Machine Learning and Cybernetics. IEEE, 2006. http://dx.doi.org/10.1109/icmlc.2006.258365.

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Murthy, Akshay, Curtis Green, Radu Stoleru, Suman Bhunia, Charles Swanson, and Theodora Chaspari. "Machine Learning-based Irrigation Control Optimization." In BuildSys '19: The 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation. ACM, 2019. http://dx.doi.org/10.1145/3360322.3360854.

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Reports on the topic "Energy Efficient Machine Learning"

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Burlig, Fiona, Christopher Knittel, David Rapson, Mar Reguant, and Catherine Wolfram. Machine Learning from Schools about Energy Efficiency. National Bureau of Economic Research, 2017. http://dx.doi.org/10.3386/w23908.

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Heinz, M. Improving High-Energy Particle Detectorswith Machine Learning. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1670544.

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Smith, Justin, Nicholas Lubbers, Aidan Thompson, and Kipton Barros. Simple and efficient algorithms for training machine learning potentials to force data. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1763572.

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Arumugam, Kamesh. Efficient Machine Learning Approach for Optimizing Scientific Computing Applications on Emerging HPC Architectures. Office of Scientific and Technical Information (OSTI), 2017. http://dx.doi.org/10.2172/1422715.

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Mueller, Juliane. Machine Learning to Enable Efficient Uncertainty Quantification, Data Assimilation, and Informed Data Acquisition. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1769743.

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Angerami, Aaron, Piyush Karande, Wojtek Fedorko, et al. Machine Learning for Pion Identification and Energy Calibration with the ATLAS Detector. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1638440.

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Knittel, Christopher, and Samuel Stolper. Using Machine Learning to Target Treatment: The Case of Household Energy Use. National Bureau of Economic Research, 2019. http://dx.doi.org/10.3386/w26531.

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Chang, Chihway, Alex Drlica-Wagner, Brian Nord, Donah, Michelle Wang, and Michael H. L. S. Wang. A Machine Learning Approach to the Detection of Ghosting Artifacts in Dark Energy Survey Images. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1594126.

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Karali, Nihan, Won Young Park, and Michael A. McNeil. Using learning curves on energy-efficient technologies to estimate future energy savings and emission reduction potentials in the U.S. iron and steel industry. Office of Scientific and Technical Information (OSTI), 2015. http://dx.doi.org/10.2172/1372638.

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Fan, Jiwen, Zhangshuan Hou, Paul O'Gorman, et al. Develop a weather-aware climate model to understand and predict extremes and associated power outages and renewable energy shortageswith uncertainty-aware and physics-informed machine learning. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1769695.

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