Results 11 to 20 of about 60,322 (264)
Energy-Efficient Deep Learning for Cloud Detection Onboard Nanosatellite
IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingDeep learning has been increasingly utilized for cloud detection in Earth observation nanosatellites, offering effective solutions to enhance mission performance.
Imane Khalil +4 more
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Energy-Efficient Ultra-Dense Network With Deep Reinforcement Learning [PDF]
IEEE Transactions on Wireless Communications, 2022 Submitted to IEEE Transactions on Wireless Communications. Copyright 2021 IEEE. Personal use of this material is permitted.
Hyungyu Ju +3 more
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CoRR, 2023 Deep Learning has enabled many advances in machine learning applications in the last few years. However, since current Deep Learning algorithms require much energy for computations, there are growing concerns about the associated environmental costs. Energy-efficient Deep Learning has received much attention from researchers and has already made much ...
Vanessa Mehlin +2 more
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Energy-Efficient GPU Clusters Scheduling for Deep Learning
CoRR, 2023 Training deep neural networks (DNNs) is a major workload in datacenters today, resulting in a tremendously fast growth of energy consumption. It is important to reduce the energy consumption while completing the DL training jobs early in data centers. In this paper, we propose PowerFlow, a GPU clusters scheduler that reduces the average Job Completion ...
Diandian Gu +4 more
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Energy-efficient parking analytics system using deep reinforcement learning [PDF]
Proceedings of the 8th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, 2021 Advances in deep vision techniques and ubiquity of smart cameras will drive the next generation of video analytics. However, video analytics applications consume vast amounts of energy as both deep learning techniques and cameras are power-hungry.
Yoones Rezaei +2 more
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Conditional Deep Learning for Energy-Efficient and Enhanced Pattern Recognition [PDF]
Proceedings of the 2016 Design, Automation & Test in Europe Conference & Exhibition (DATE), 2016 Deep learning neural networks have emerged as one of the most powerful classification tools for vision related applications. However, the computational and energy requirements associated with such deep nets can be quite high, and hence their energy-efficient implementation is of great interest.
Priyadarshini Panda +2 more
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Structured Convolution Matrices for Energy-efficient Deep learning
CoRR, 2016 We derive a relationship between network representation in energy-efficient neuromorphic architectures and block Toplitz convolutional matrices. Inspired by this connection, we develop deep convolutional networks using a family of structured convolutional matrices and achieve state-of-the-art trade-off between energy efficiency and classification ...
Rathinakumar Appuswamy +8 more
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Deep Learning with Energy-efficient Binary Gradient Cameras
CoRR, 2016 Power consumption is a critical factor for the deployment of embedded computer vision systems. We explore the use of computational cameras that directly output binary gradient images to reduce the portion of the power consumption allocated to image sensing.
Suren Jayasuriya +3 more
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Deep Learning for Energy Efficiency in Smart Grids
American Journal of Artificial Intelligence and Neural Networks, 2022 Smart grids represent the future of energy distribution, offering enhanced efficiency, reliability, and sustainability. Deep learning techniques are playing a crucial role in optimizing energy consumption and distribution within these grids by enabling predictive modeling, fault detection, and demand forecasting. This article explores how deep learning
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Deep Reinforcement Learning for Energy-Efficient Networking with Reconfigurable Intelligent Surfaces [PDF]
ICC 2020 - 2020 IEEE International Conference on Communications (ICC), 2020 When deployed as reflectors for existing wireless base stations (BSs), reconfigurable intelligent surfaces (RISs) can be a promising approach to achieve high spectrum and energy efficiency. However, due to the large number of RIS elements, the joint optimization of the BS and reflector RIS configuration is challenging. In essence, the BS transmit power
Gilsoo Lee +4 more
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