zTT: learning-based DVFS with zero thermal throttling for mobile devices
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[1] Amit Kumar Singh,et al. TEEM: Online Thermal- and Energy-Efficiency Management on CPU-GPU MPSoCs , 2019, 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE).
[2] Laurence T. Yang,et al. Energy-Efficient Scheduling for Real-Time Systems Based on Deep Q-Learning Model , 2019, IEEE Transactions on Sustainable Computing.
[3] Tajana Simunic,et al. Modeling and mitigation of extra-SoC thermal coupling effects and heat transfer variations in mobile devices , 2015, 2015 IEEE/ACM International Conference on Computer-Aided Design (ICCAD).
[4] Wei Liu,et al. Adaptive power management using reinforcement learning , 2009, 2009 IEEE/ACM International Conference on Computer-Aided Design - Digest of Technical Papers.
[5] Anuj Pathania,et al. Power-performance modelling of mobile gaming workloads on heterogeneous MPSoCs , 2015, 2015 52nd ACM/EDAC/IEEE Design Automation Conference (DAC).
[6] Tulika Mitra,et al. OPTiC: Optimizing Collaborative CPU–GPU Computing on Mobile Devices With Thermal Constraints , 2019, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.
[7] Ying Tan,et al. Achieving autonomous power management using reinforcement learning , 2013, TODE.
[8] Ümit Y. Ogras,et al. Predictive dynamic thermal and power management for heterogeneous mobile platforms , 2015, 2015 Design, Automation & Test in Europe Conference & Exhibition (DATE).
[9] Ümit Y. Ogras,et al. Power and Thermal Analysis of Commercial Mobile Platforms: Experiments and Case Studies , 2019, 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE).
[10] Qiang Wang,et al. HKBU Institutional Repository , 2018 .
[11] Xin Fu,et al. Redefining QoS and customizing the power management policy to satisfy individual mobile users , 2016, 2016 49th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO).
[12] Onur Sahin,et al. Providing sustainable performance in thermally constrained mobile devices , 2016, 2016 14th ACM/IEEE Symposium on Embedded Systems For Real-time Multimedia (ESTIMedia).
[13] Julie A. Kientz,et al. Developing and Validating the User Burden Scale: A Tool for Assessing User Burden in Computing Systems , 2016, CHI.
[14] Jorg Henkel,et al. Application and Thermal-reliability-aware Reinforcement Learning Based Multi-core Power Management , 2019, ACM J. Emerg. Technol. Comput. Syst..
[15] Patrick Mochel. The sysfs Filesystem , 2005 .
[16] Zhiping Jia,et al. Cooperative DVFS for energy-efficient HEVC decoding on embedded CPU-GPU architecture , 2017, 2017 54th ACM/EDAC/IEEE Design Automation Conference (DAC).
[17] Jinwoo Shin,et al. MetaSense: few-shot adaptation to untrained conditions in deep mobile sensing , 2019, SenSys.
[18] Lei Yang,et al. Frequency Scaling for Processor Power Efficiency , 2013 .
[19] Krishna Sekar,et al. Power and thermal challenges in mobile devices , 2013, MobiCom.
[20] Geoff V. Merrett,et al. Accurate and Stable Run-Time Power Modeling for Mobile and Embedded CPUs , 2017, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.
[21] Hojung Cha,et al. Graphics-aware Power Governing for Mobile Devices , 2019, MobiSys.
[22] Marc G. Bellemare,et al. The Arcade Learning Environment: An Evaluation Platform for General Agents , 2012, J. Artif. Intell. Res..
[23] Umit Y. Ogras,et al. Dynamic Power Budgeting for Mobile Systems Running Graphics Workloads , 2018, IEEE Transactions on Multi-Scale Computing Systems.
[24] Naehyuck Chang,et al. Dynamic thermal management in mobile devices considering the thermal coupling between battery and application processor , 2013, 2013 IEEE/ACM International Conference on Computer-Aided Design (ICCAD).
[25] Muhammad Shafique,et al. Improving mobile gaming performance through cooperative CPU-GPU thermal management , 2016, 2016 53nd ACM/EDAC/IEEE Design Automation Conference (DAC).
[26] Lothar Thiele,et al. Maestro: Autonomous QoS Management for Mobile Applications Under Thermal Constraints , 2019, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.
[27] Sergey Levine,et al. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks , 2017, ICML.
[28] Chaitali Chakrabarti,et al. A Deep Q-Learning Approach for Dynamic Management of Heterogeneous Processors , 2019, IEEE Computer Architecture Letters.
[29] Ali Farhadi,et al. You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[30] Sung-Ju Lee,et al. Fire in Your Hands: Understanding Thermal Behavior of Smartphones , 2019, MobiCom.
[31] Shane Legg,et al. Human-level control through deep reinforcement learning , 2015, Nature.
[32] Yansong Feng,et al. Proteus: network-aware web browsing on heterogeneous mobile systems , 2018, CoNEXT.
[33] Ali Farhadi,et al. YOLOv3: An Incremental Improvement , 2018, ArXiv.
[34] Michael Kishinevsky,et al. A control-theoretic approach for energy efficient CPU-GPU subsystem in mobile platforms , 2015, 2015 52nd ACM/EDAC/IEEE Design Automation Conference (DAC).
[35] Nikil D. Dutt,et al. Synergistic CPU-GPU Frequency Capping for Energy-Efficient Mobile Games , 2018, ACM Trans. Embed. Comput. Syst..
[36] Dilip Krishnaswamy,et al. PROMETHEUS: A Proactive Method for Thermal Management of Heterogeneous MPSoCs , 2013, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.
[37] Mehdi Kamal,et al. A heuristic machine learning-based algorithm for power and thermal management of heterogeneous MPSoCs , 2015, 2015 IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED).
[38] Umit Y. Ogras,et al. Algorithmic Optimization of Thermal and Power Management for Heterogeneous Mobile Platforms , 2018, IEEE Transactions on Very Large Scale Integration (VLSI) Systems.