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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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Exploration of Energy Efficient Hardware and Algorithms for Deep Learning
, 2019 Deep Neural Networks (DNNs) have emerged as the state-of-the-art technique in a wide range of machine learning tasks for analytics and computer vision in the next generation of embedded (mobile, IoT, wearable) devices. Despite their success, they suffer from high energy requirements both in inference and training.
Sarwar, Syed Shakib
core +3 more sources
Frontiers in Robotics and AI, 2022 Underwater snake robots have received attention because of their unique mechanics and locomotion patterns. Given their highly redundant degrees of freedom, designing an energy-efficient gait has been a main challenge for the long-term autonomy of ...
Chu Zheng +2 more
doaj +1 more source
Energy-efficient deep learning model for fruit freshness detection
IAES International Journal of Artificial Intelligence (IJ-AI), 2023 Fruits are usually used as complementary foods because they contain good nutrients such as protein and vitamins. In addition to having good content, it turns out that there are potentially harmful microorganisms contained in fruits caused by decay ...
Febri Valentino +3 more
semanticscholar +1 more source
Towards efficient and effective renewable energy prediction via deep learning
Energy Reports, 2022 Renewable energy (RE) offers major environmental and economic benefits compared to nuclear and fuel-based energy; however, the data used for RE include significant randomness, intermittent behaviour, and strong-volatility, hindering their integration ...
Zulfiqar Ahmad Khan +4 more
doaj +1 more source
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
openaire +4 more sources
Review on Deep Learning Research and Applications in Wind and Wave Energy
Energies, 2022 Wind energy and wave energy are considered to have enormous potential as renewable energy sources in the energy system to make great contributions in transitioning from fossil fuel to renewable energy.
Chengcheng Gu, Hua Li
doaj +1 more source
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
openaire +3 more sources
ThUnderVolt: Enabling Aggressive Voltage Underscaling and Timing Error Resilience for Energy Efficient Deep Learning Accelerators [PDF]
Design Automation Conference, 2018 Hardware accelerators are being increasingly deployed to boost the performance and energy efficiency of deep neural network (DNN) inference. In this paper we propose Thundervolt, a new framework that enables aggressive voltage underscaling of high ...
Jeff Zhang +3 more
semanticscholar +1 more source
Uncovering Energy-Efficient Practices in Deep Learning Training: Preliminary Steps Towards Green AI [PDF]
2023 IEEE/ACM 2nd International Conference on AI Engineering – Software Engineering for AI (CAIN), 2023 Modern AI practices all strive towards the same goal: better results. In the context of deep learning, the term "results" often refers to the achieved accuracy on a competitive problem set.
Tim Yarally +4 more
semanticscholar +1 more source