{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T23:46:12Z","timestamp":1783035972607,"version":"3.54.6"},"publisher-location":"New York, NY, USA","reference-count":48,"publisher":"ACM","license":[{"start":{"date-parts":[[2019,6,15]],"date-time":"2019-06-15T00:00:00Z","timestamp":1560556800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Hong Kong RGC","award":["HKBU 12200418"],"award-info":[{"award-number":["HKBU 12200418"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2019,6,15]]},"DOI":"10.1145\/3307772.3328315","type":"proceedings-article","created":{"date-parts":[[2019,6,13]],"date-time":"2019-06-13T12:09:40Z","timestamp":1560427780000},"page":"315-325","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":69,"title":["The Impact of GPU DVFS on the Energy and Performance of Deep Learning"],"prefix":"10.1145","author":[{"given":"Zhenheng","family":"Tang","sequence":"first","affiliation":[{"name":"Department of Computer Science, Hong Kong Baptist University, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuxin","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Hong Kong Baptist University, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiang","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Hong Kong Baptist University, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaowen","family":"Chu","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Hong Kong Baptist University, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2019,6,15]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/IPDPS.2014.23"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/2962131"},{"key":"e_1_3_2_1_3_1","volume-title":"Proceedings of The 9th Asian Conference on Machine Learning (ACML)","author":"Cai E.","year":"2017","unstructured":"E. Cai , D. Juan , D. Stamoulis , and D. Marculescu . 2017. Neuralpower: Predict and deploy energy-efficient convolutional neural networks . In Proceedings of The 9th Asian Conference on Machine Learning (ACML) , Seoul, Korea , November , 2017 . E. Cai, D. Juan, D. Stamoulis, and D. Marculescu. 2017. Neuralpower: Predict and deploy energy-efficient convolutional neural networks. In Proceedings of The 9th Asian Conference on Machine Learning (ACML), Seoul, Korea, November, 2017."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3077839.3077855"},{"key":"e_1_3_2_1_5_1","volume-title":"High Performance Convolutional Neural Networks for Document Processing. In Tenth International Workshop on Frontiers in Handwriting Recognition","author":"Chellapilla K.","year":"2006","unstructured":"K. Chellapilla , S. Puri , and P. Simard . 2006 . High Performance Convolutional Neural Networks for Document Processing. In Tenth International Workshop on Frontiers in Handwriting Recognition , La Baule, France , October , 2006 . K. Chellapilla, S. Puri, and P. Simard. 2006. High Performance Convolutional Neural Networks for Document Processing. In Tenth International Workshop on Frontiers in Handwriting Recognition, La Baule, France, October, 2006."},{"key":"e_1_3_2_1_6_1","volume-title":"Thirty-Second AAAI Conference on Artificial Intelligence (AAAI)","author":"Chen C.","year":"2018","unstructured":"C. Chen , J. Choi , D. Brand , A. Agrawal , W. Zhang , and K. Gopalakrishnan . 2018. Adacomp: Adaptive residual gradient compression for data-parallel distributed training . In Thirty-Second AAAI Conference on Artificial Intelligence (AAAI) , New Orleans, Louisiana, USA , February , 2018 . C. Chen, J. Choi, D. Brand, A. Agrawal, W. Zhang, and K. Gopalakrishnan. 2018. Adacomp: Adaptive residual gradient compression for data-parallel distributed training. In Thirty-Second AAAI Conference on Artificial Intelligence (AAAI), New Orleans, Louisiana, USA, February, 2018."},{"key":"e_1_3_2_1_7_1","unstructured":"S. Chetlur C. Woolley P. Vandermersch J. Cohen J. Tran B. Catanzaro and E. Shelhamer. 2014. cudnn: Efficient primitives for deep learning. arXiv preprint arXiv:1410.0759 (2014).  S. Chetlur C. Woolley P. Vandermersch J. Cohen J. Tran B. Catanzaro and E. Shelhamer. 2014. cudnn: Efficient primitives for deep learning. arXiv preprint arXiv:1410.0759 (2014)."},{"key":"e_1_3_2_1_8_1","unstructured":"D. Das S. Avancha D. Mudigere K. Vaidynathan S. Sridharan D. Kalamkar B. Kaul and P. Dubey. 2016. Distributed deep learning using synchronous stochastic gradient descent. arXiv preprint arXiv:1602.06709 (2016).  D. Das S. Avancha D. Mudigere K. Vaidynathan S. Sridharan D. Kalamkar B. Kaul and P. Dubey. 2016. Distributed deep learning using synchronous stochastic gradient descent. arXiv preprint arXiv:1602.06709 (2016)."},{"key":"e_1_3_2_1_9_1","volume-title":"Nevada","author":"Dean J.","year":"2012","unstructured":"J. Dean , G. Corrado , R. Monga , K. Chen , M. Devin , M. Mao , A. Senior , P. Tucker , K. Yang , and Q. V. Le . 2012. Large Scale Distributed Deep Networks. In Advances in Neural Information Processing Systems (NeurIPS), Lake Tahoe , Nevada , United States , December , 2012 . J. Dean, G. Corrado, R. Monga, K. Chen, M. Devin, M. Mao, A. Senior, P. Tucker, K. Yang, and Q. V. Le. 2012. Large Scale Distributed Deep Networks. In Advances in Neural Information Processing Systems (NeurIPS), Lake Tahoe, Nevada, United States, December, 2012."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICPP.2013.98"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.169"},{"key":"e_1_3_2_1_13_1","unstructured":"P. Goyal P. Doll\u00e1r R. Girshick P. Noordhuis L. Wesolowski A. Kyrola A. Tulloch Y. Jia and K. He. 2017. Accurate large minibatch SGD: training imagenet in 1 hour. arXiv preprint arXiv: 1706.02677 (2017).  P. Goyal P. Doll\u00e1r R. Girshick P. Noordhuis L. Wesolowski A. Kyrola A. Tulloch Y. Jia and K. He. 2017. Accurate large minibatch SGD: training imagenet in 1 hour. arXiv preprint arXiv: 1706.02677 (2017)."},{"key":"e_1_3_2_1_14_1","volume-title":"Deep Residual Learning for Image Recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","author":"He K.","year":"2016","unstructured":"K. He , X. Zhang , S. Ren , and J. Sun . 2016 . Deep Residual Learning for Image Recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , Las Vegas, NV, USA , June , 2016 . K. He, X. Zhang, S. Ren, and J. Sun. 2016. Deep Residual Learning for Image Recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, June, 2016."},{"key":"e_1_3_2_1_15_1","volume-title":"Highly Scalable Deep Learning Training System with Mixed-Precision: Training ImageNet in Four Minutes. In NeurIPS Workshop on Systems for ML and Open Source Software","author":"Jia X.","year":"2018","unstructured":"X. Jia , S. Song , W. He , Y. Wang , H. Rong , F. Zhou , L. Xie , Z. Guo , Y. Yang , and L. Yu . 2018 . Highly Scalable Deep Learning Training System with Mixed-Precision: Training ImageNet in Four Minutes. In NeurIPS Workshop on Systems for ML and Open Source Software , Montr\u00e9al, Canada , December , 2018 . X. Jia, S. Song, W. He, Y. Wang, H. Rong, F. Zhou, L. Xie, Z. Guo, Y. Yang, and L. Yu. 2018. Highly Scalable Deep Learning Training System with Mixed-Precision: Training ImageNet in Four Minutes. In NeurIPS Workshop on Systems for ML and Open Source Software, Montr\u00e9al, Canada, December, 2018."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/2647868.2654889"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/GreenCom-CPSCom.2010.143"},{"key":"e_1_3_2_1_18_1","volume-title":"ImageNet Classification with Deep Convolutional Neural Networks. In Neural Information Processing Systems (NeurIPS)","author":"Krizhevsky A.","year":"2009","unstructured":"A. Krizhevsky , I. Sutskever , and G. E. Hinton . 2012 . ImageNet Classification with Deep Convolutional Neural Networks. In Neural Information Processing Systems (NeurIPS) , Miami, Florida, USA , June , 2009 . A. Krizhevsky, I. Sutskever, and G. E. Hinton. 2012. ImageNet Classification with Deep Convolutional Neural Networks. In Neural Information Processing Systems (NeurIPS), Miami, Florida, USA, June, 2009."},{"key":"e_1_3_2_1_19_1","volume-title":"Fast Algorithms for Convolutional Neural Networks. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Lavin A.","year":"2016","unstructured":"A. Lavin and S. Gray . 2016 . Fast Algorithms for Convolutional Neural Networks. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , Las Vegas, NV, USA , June , 2016 . A. Lavin and S. Gray. 2016. Fast Algorithms for Convolutional Neural Networks. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, June, 2016."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"crossref","unstructured":"Y. LeCun Y. Bengio and G. Hinton. 2015. Deep learning. Nature 521 7553 (2015) 436--444.  Y. LeCun Y. Bengio and G. Hinton. 2015. Deep learning. Nature 521 7553 (2015) 436--444.","DOI":"10.1038\/nature14539"},{"key":"e_1_3_2_1_21_1","volume-title":"Improving Throughput of Power-Constrained GPUs Using Dynamic Voltage\/Frequency and Core Scaling. In 2011 International Conference on PACT","author":"Lee J.","year":"2011","unstructured":"J. Lee , V. Sathisha , M. J. Schulte , K. Compton , and N. Kim . 2011 . Improving Throughput of Power-Constrained GPUs Using Dynamic Voltage\/Frequency and Core Scaling. In 2011 International Conference on PACT , Galveston, TX, USA , October , 2011 . J. Lee, V. Sathisha, M. J. Schulte, K. Compton, and N. Kim. 2011. Improving Throughput of Power-Constrained GPUs Using Dynamic Voltage\/Frequency and Core Scaling. In 2011 International Conference on PACT, Galveston, TX, USA, October, 2011."},{"key":"e_1_3_2_1_22_1","volume-title":"Montreal","author":"Lee S.","year":"2014","unstructured":"S. Lee , J. K. Kim , X. Zheng , Q. Ho , G. A. Gibson , and E. P. Xing . 2014. On Model Parallelization and Scheduling Strategies for Distributed Machine Learning. In Advances in Neural Information Processing Systems (NeurIPS) , Montreal , Quebec, Canada , December , 2014 . S. Lee, J. K. Kim, X. Zheng, Q. Ho, G. A. Gibson, and E. P. Xing. 2014. On Model Parallelization and Scheduling Strategies for Distributed Machine Learning. In Advances in Neural Information Processing Systems (NeurIPS), Montreal, Quebec, Canada, December, 2014."},{"key":"e_1_3_2_1_23_1","volume-title":"Evaluating the Energy Efficiency of Deep Convolutional Neural Networks on CPUs and GPUs. In 2016 IEEE International Conferences on Sustainable Computing and Communications (SustainCom)","author":"Li D.","year":"2016","unstructured":"D. Li , X. Chen , M. Becchi , and Z. Zong . 2016 . Evaluating the Energy Efficiency of Deep Convolutional Neural Networks on CPUs and GPUs. In 2016 IEEE International Conferences on Sustainable Computing and Communications (SustainCom) , Atlanta, GA, USA , October , 2016 . D. Li, X. Chen, M. Becchi, and Z. Zong. 2016. Evaluating the Energy Efficiency of Deep Convolutional Neural Networks on CPUs and GPUs. In 2016 IEEE International Conferences on Sustainable Computing and Communications (SustainCom), Atlanta, GA, USA, October, 2016."},{"key":"e_1_3_2_1_24_1","volume-title":"Scaling Distributed Machine Learning with the Parameter Server. In 11th USENIX Symposium on Operating Systems Design and Implementation, OSDI '14","author":"Li M.","year":"2014","unstructured":"M. Li , D. G. Andersen , J. W. Park , A. J. Smola , A. Ahmed , V. Josifovski , J. Long , E. J. Shekita , and B. Su . 2014 . Scaling Distributed Machine Learning with the Parameter Server. In 11th USENIX Symposium on Operating Systems Design and Implementation, OSDI '14 , Broomfield, CO, USA , October , 2014 . M. Li, D. G. Andersen, J. W. Park, A. J. Smola, A. Ahmed, V. Josifovski, J. Long, E. J. Shekita, and B. Su. 2014. Scaling Distributed Machine Learning with the Parameter Server. In 11th USENIX Symposium on Operating Systems Design and Implementation, OSDI '14, Broomfield, CO, USA, October, 2014."},{"key":"e_1_3_2_1_25_1","volume-title":"Performance Analysis of GPU-Based Convolutional Neural Networks. In 2016 45th International Conference on Parallel Processing (ICPP)","author":"Li X.","year":"2016","unstructured":"X. Li , G. Zhang , H. H. Huang , Z. Wang , and W. Zheng . 2016 . Performance Analysis of GPU-Based Convolutional Neural Networks. In 2016 45th International Conference on Parallel Processing (ICPP) , Philadelphia, PA, USA , August , 2016 . X. Li, G. Zhang, H. H. Huang, Z. Wang, and W. Zheng. 2016. Performance Analysis of GPU-Based Convolutional Neural Networks. In 2016 45th International Conference on Parallel Processing (ICPP), Philadelphia, PA, USA, August, 2016."},{"key":"e_1_3_2_1_26_1","volume-title":"6th International Conference on Learning Representations (ICLR), BC","author":"Lin Y.","year":"2018","unstructured":"Y. Lin , S. Han , H. Mao , Y. Wang , and W.J. Dally . 2018. Deep gradient compression: Reducing the communication bandwidth for distributed training . In 6th International Conference on Learning Representations (ICLR), BC , Canada , April 30 - May 3, 2018 . Y. Lin, S. Han, H. Mao, Y. Wang, and W.J. Dally. 2018. Deep gradient compression: Reducing the communication bandwidth for distributed training. In 6th International Conference on Learning Representations (ICLR), BC, Canada, April 30 - May 3, 2018."},{"key":"e_1_3_2_1_27_1","volume-title":"14th European conference on computer vision (ECCV)","author":"Liu W.","year":"2016","unstructured":"W. Liu , D. Anguelov , D. Erhan , C. Szegedy , S. Reed , C. Fu , and A. C. Berg . 2016. Ssd: Single shot multibox detector . In 14th European conference on computer vision (ECCV) , Amsterdam, The Netherlands , October , 2016 . W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C. Fu, and A. C. Berg. 2016. Ssd: Single shot multibox detector. In 14th European conference on computer vision (ECCV), Amsterdam, The Netherlands, October, 2016."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2016.2549523"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2017.8057205"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.dcan.2016.10.001"},{"key":"e_1_3_2_1_31_1","volume-title":"Ilan","author":"Mei X.","year":"2014","unstructured":"X. Mei , K. Zhao , C. Liu , and X. Chu . 2014. Benchmarking the memory hierarchy of modern GPUs. In Network and Parallel Computing - 11th IFIP , Ilan , Taiwan , September , 2014 . X. Mei, K. Zhao, C. Liu, and X. Chu. 2014. Benchmarking the memory hierarchy of modern GPUs. In Network and Parallel Computing - 11th IFIP, Ilan, Taiwan, September, 2014."},{"key":"e_1_3_2_1_32_1","volume-title":"2nd International Conference on Learning Representations (ICLR), Banff, AB","author":"Micha\u00ebl M.","year":"2014","unstructured":"M. Micha\u00ebl , M. Henaff , and Y. LeCun . 2014. Fast training of convolutional networks through ffts . In 2nd International Conference on Learning Representations (ICLR), Banff, AB , Canada , April , 2014 . M. Micha\u00ebl, M. Henaff, and Y. LeCun. 2014. Fast training of convolutional networks through ffts. In 2nd International Conference on Learning Representations (ICLR), Banff, AB, Canada, April, 2014."},{"key":"e_1_3_2_1_33_1","unstructured":"NVIDIA. 2018. NVIDIA Management Library. {Online} https:\/\/developer.nvidia.com\/nvidia-management-library-nvml.  NVIDIA. 2018. NVIDIA Management Library. {Online} https:\/\/developer.nvidia.com\/nvidia-management-library-nvml."},{"key":"e_1_3_2_1_34_1","unstructured":"NVIDIA. 2018. NVIDIA System Management Interface (nvidia-smi). {Online} https:\/\/developer.nvidia.com\/nvidia-system-management-interface.  NVIDIA. 2018. NVIDIA System Management Interface (nvidia-smi). {Online} https:\/\/developer.nvidia.com\/nvidia-system-management-interface."},{"key":"e_1_3_2_1_35_1","unstructured":"Orbmu2k. 2016. NVIDIA Inspector. {Online} http:\/\/blog.orbmu2k.de\/tools\/nvidia-inspector-tool.  Orbmu2k. 2016. NVIDIA Inspector. {Online} http:\/\/blog.orbmu2k.de\/tools\/nvidia-inspector-tool."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/2749469.2750404"},{"key":"e_1_3_2_1_37_1","volume-title":"Real-Time Object Detection. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Redmon J.","year":"2016","unstructured":"J. Redmon , S. Divvala , R. Girshick , and A. Farhadi . 2016. You Only Look Once: Unified , Real-Time Object Detection. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , Las Vegas, NV, USA , June , 2016 . J. Redmon, S. Divvala, R. Girshick, and A. Farhadi. 2016. You Only Look Once: Unified, Real-Time Object Detection. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, June, 2016."},{"key":"e_1_3_2_1_38_1","volume-title":"Evaluation of Deep Learning Frameworks Over Different HPC Architectures. In 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)","author":"Shams S.","year":"2017","unstructured":"S. Shams , R. Platania , K. Lee , and S. Park . 2017 . Evaluation of Deep Learning Frameworks Over Different HPC Architectures. In 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS) , Atlanta, GA, USA , June , 2017 . S. Shams, R. Platania, K. Lee, and S. Park. 2017. Evaluation of Deep Learning Frameworks Over Different HPC Architectures. In 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS), Atlanta, GA, USA, June, 2017."},{"key":"e_1_3_2_1_39_1","volume-title":"Performance Modeling and Evaluation of Distributed Deep Learning Frameworks on GPUs. In 4th Intl Conf on Big Data Intelligence and Computing(DataCom)","author":"Shi S.","year":"2018","unstructured":"S. Shi , Q. Wang , and X. Chu . 2018 . Performance Modeling and Evaluation of Distributed Deep Learning Frameworks on GPUs. In 4th Intl Conf on Big Data Intelligence and Computing(DataCom) , Athens, Greece , August , 2018 . S. Shi, Q. Wang, and X. Chu. 2018. Performance Modeling and Evaluation of Distributed Deep Learning Frameworks on GPUs. In 4th Intl Conf on Big Data Intelligence and Computing(DataCom), Athens, Greece, August, 2018."},{"key":"e_1_3_2_1_40_1","volume-title":"Benchmarking State-of-the-Art Deep Learning Software Tools. In 2016 7th International Conference on Cloud Computing and Big Data (CCBD)","author":"Shi S.","year":"2016","unstructured":"S. Shi , Q. Wang , P. Xu , and X. Chu . 2016 . Benchmarking State-of-the-Art Deep Learning Software Tools. In 2016 7th International Conference on Cloud Computing and Big Data (CCBD) , Macau, China , November , 2016 . S. Shi, Q. Wang, P. Xu, and X. Chu. 2016. Benchmarking State-of-the-Art Deep Learning Software Tools. In 2016 7th International Conference on Cloud Computing and Big Data (CCBD), Macau, China, November, 2016."},{"key":"e_1_3_2_1_41_1","volume-title":"3rd International Conference on Learning Representations (ICLR)","author":"Simonyan K.","year":"2015","unstructured":"K. Simonyan and A. Zisserman . 2015. Very deep convolutional networks for large-scale image recognition . In 3rd International Conference on Learning Representations (ICLR) , San Diego, CA, USA , May , 2015 ,. K. Simonyan and A. Zisserman. 2015. Very deep convolutional networks for large-scale image recognition. In 3rd International Conference on Learning Representations (ICLR), San Diego, CA, USA, May, 2015,."},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2017.2761740"},{"key":"e_1_3_2_1_43_1","volume-title":"Going Deeper With Convolutions. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Szegedy C.","year":"2015","unstructured":"C. Szegedy , W. Liu , Y. Jia , P. Sermanet , S. Reed , D. Anguelov , D. Erhan , V. Vanhoucke , and A. Rabinovich . 2015 . Going Deeper With Convolutions. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , Boston, MA, USA , June , 2015 . C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich. 2015. Going Deeper With Convolutions. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, June, 2015."},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/3152042.3152066"},{"key":"e_1_3_2_1_45_1","volume-title":"GPGPU Performance Estimation with Core and Memory Frequency Scaling. In 2018 IEEE 24th International Conference on Parallel and Distributed Systems (ICPADS)","author":"Wang Q.","year":"2018","unstructured":"Q. Wang and X. Chu . 2018 . GPGPU Performance Estimation with Core and Memory Frequency Scaling. In 2018 IEEE 24th International Conference on Parallel and Distributed Systems (ICPADS) , Singapore , December , 2018 . Q. Wang and X. Chu. 2018. GPGPU Performance Estimation with Core and Memory Frequency Scaling. In 2018 IEEE 24th International Conference on Parallel and Distributed Systems (ICPADS), Singapore, December, 2018."},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/3077839.3077858"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/3225058.3225069"},{"key":"e_1_3_2_1_48_1","volume-title":"Neural Architecture Search with Reinforcement Learning. In 5th International Conference on Learning Representations (ICLR)","author":"Zoph B.","year":"2017","unstructured":"B. Zoph and Q. V. Le . 2017 . Neural Architecture Search with Reinforcement Learning. In 5th International Conference on Learning Representations (ICLR) , Toulon, France , April , 2017 . B. Zoph and Q. V. Le. 2017. Neural Architecture Search with Reinforcement Learning. In 5th International Conference on Learning Representations (ICLR), Toulon, France, April, 2017."}],"event":{"name":"e-Energy '19: The Tenth ACM International Conference on Future Energy Systems","location":"Phoenix AZ USA","acronym":"e-Energy '19","sponsor":["SIGEnergy ACM Special Interest Group on Energy Systems and Informatics"]},"container-title":["Proceedings of the Tenth ACM International Conference on Future Energy Systems"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3307772.3328315","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3307772.3328315","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T23:54:07Z","timestamp":1750204447000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3307772.3328315"}},"subtitle":["an Empirical Study"],"short-title":[],"issued":{"date-parts":[[2019,6,15]]},"references-count":48,"alternative-id":["10.1145\/3307772.3328315","10.1145\/3307772"],"URL":"https:\/\/doi.org\/10.1145\/3307772.3328315","relation":{},"subject":[],"published":{"date-parts":[[2019,6,15]]},"assertion":[{"value":"2019-06-15","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}