{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:16:38Z","timestamp":1750220198708,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":29,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,8,29]],"date-time":"2022-08-29T00:00:00Z","timestamp":1661731200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Center for Trust Internet and Community, National University of Singapore","award":["CTIC-RP-20-03"],"award-info":[{"award-number":["CTIC-RP-20-03"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,8,29]]},"DOI":"10.1145\/3545008.3545020","type":"proceedings-article","created":{"date-parts":[[2023,1,15]],"date-time":"2023-01-15T01:04:08Z","timestamp":1673744648000},"page":"1-13","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["HARL: Hierarchical Adaptive Reinforcement Learning Based Auto Scheduler for Neural Networks"],"prefix":"10.1145","author":[{"given":"Zining","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Computing, National University of Singapore, Singapore and Center for Trust Internet and Community, National University of Singapore, Singapore"}]},{"given":"Bingsheng","family":"He","sequence":"additional","affiliation":[{"name":"School of Computing, National University of Singapore, Singapore"}]},{"given":"Zhenjie","family":"Zhang","sequence":"additional","affiliation":[{"name":"Neuron Mobility Pte. Ltd., Singapore"}]}],"member":"320","published-online":{"date-parts":[[2023,1,13]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"2022. Auto-scheduler in TVM v0.8.0. https:\/\/github.com\/apache\/tvm\/tree\/v0.8.0\/python\/tvm\/auto_scheduler. (Accessed on 03\/29\/2022)."},{"key":"e_1_3_2_1_2_1","unstructured":"2022. GEMM - Wolfram Language Documentation. https:\/\/reference.wolfram.com\/language\/LowLevelLinearAlgebra\/ref\/GEMM.html. (Accessed on 03\/29\/2022)."},{"key":"e_1_3_2_1_3_1","unstructured":"2022. oneapi-src\/oneDNN: oneAPI Deep Neural Network Library (oneDNN). https:\/\/github.com\/oneapi-src\/oneDNN. (Accessed on 03\/29\/2022)."},{"key":"e_1_3_2_1_4_1","unstructured":"2022. PPO-PyTorch. https:\/\/github.com\/nikhilbarhate99\/PPO-PyTorch. (Accessed on 03\/29\/2022)."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3306346.3322967"},{"key":"e_1_3_2_1_6_1","volume-title":"Chameleon: Adaptive code optimization for expedited deep neural network compilation. arXiv preprint arXiv:2001.08743(2020).","author":"Ahn Byung\u00a0Hoon","year":"2020","unstructured":"Byung\u00a0Hoon Ahn, Prannoy Pilligundla, Amir Yazdanbakhsh, and Hadi Esmaeilzadeh. 2020. Chameleon: Adaptive code optimization for expedited deep neural network compilation. arXiv preprint arXiv:2001.08743(2020)."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"e_1_3_2_1_8_1","volume-title":"Learning to optimize tensor programs. Advances in Neural Information Processing Systems 31","author":"Chen Tianqi","year":"2018","unstructured":"Tianqi Chen, Lianmin Zheng, Eddie Yan, Ziheng Jiang, Thierry Moreau, Luis Ceze, Carlos Guestrin, and Arvind Krishnamurthy. 2018. Learning to optimize tensor programs. Advances in Neural Information Processing Systems 31 (2018)."},{"key":"e_1_3_2_1_9_1","unstructured":"Sharan Chetlur Cliff Woolley Philippe Vandermersch Jonathan Cohen John Tran Bryan Catanzaro and Evan Shelhamer. 2014. cudnn: Efficient primitives for deep learning. arXiv preprint arXiv:1410.0759(2014)."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.350"},{"key":"e_1_3_2_1_11_1","volume-title":"Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805(2018).","author":"Devlin Jacob","year":"2018","unstructured":"Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805(2018)."},{"key":"e_1_3_2_1_12_1","unstructured":"Vincent Dumoulin and Francesco Visin. 2016. A guide to convolution arithmetic for deep learning. arXiv preprint arXiv:1603.07285(2016)."},{"key":"e_1_3_2_1_13_1","volume-title":"Bansor: Improving Tensor Program Auto-Scheduling with Bandit Based Reinforcement Learning. In 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 273\u2013278","author":"Gao Chao","year":"2021","unstructured":"Chao Gao, Tong Mo, Taylor Zowtuk, Tanvir Sajed, Laiyuan Gong, Hanxuan Chen, Shangling Jui, and Wei Lu. 2021. Bansor: Improving Tensor Program Auto-Scheduling with Bandit Based Reinforcement Learning. In 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 273\u2013278."},{"key":"e_1_3_2_1_14_1","unstructured":"Aur\u00e9lien Garivier and Eric Moulines. 2008. On upper-confidence bound policies for non-stationary bandit problems. arXiv preprint arXiv:0805.3415(2008)."},{"key":"e_1_3_2_1_15_1","unstructured":"Awni Hannun Carl Case Jared Casper Bryan Catanzaro Greg Diamos Erich Elsen Ryan Prenger Sanjeev Satheesh Shubho Sengupta Adam Coates 2014. Deep speech: Scaling up end-to-end speech recognition. arXiv preprint arXiv:1412.5567(2014)."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"volume-title":"An introduction to genetic algorithms","author":"Mitchell Melanie","key":"e_1_3_2_1_17_1","unstructured":"Melanie Mitchell. 1998. An introduction to genetic algorithms. MIT press."},{"key":"e_1_3_2_1_18_1","volume-title":"International conference on machine learning. PMLR","author":"Mnih Volodymyr","year":"2016","unstructured":"Volodymyr Mnih, Adria\u00a0Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu. 2016. Asynchronous methods for deep reinforcement learning. In International conference on machine learning. PMLR, 1928\u20131937."},{"key":"e_1_3_2_1_19_1","unstructured":"Volodymyr Mnih Koray Kavukcuoglu David Silver Alex Graves Ioannis Antonoglou Daan Wierstra and Martin Riedmiller. 2013. Playing atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602(2013)."},{"volume-title":"Reinforcement learning","author":"Otterlo Martijn\u00a0van","key":"e_1_3_2_1_20_1","unstructured":"Martijn\u00a0van Otterlo and Marco Wiering. 2012. Reinforcement learning and markov decision processes. In Reinforcement learning. Springer, 3\u201342."},{"key":"e_1_3_2_1_21_1","volume-title":"Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems 32","author":"Paszke Adam","year":"2019","unstructured":"Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, 2019. Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019)."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00474"},{"key":"e_1_3_2_1_23_1","unstructured":"John Schulman Filip Wolski Prafulla Dhariwal Alec Radford and Oleg Klimov. 2017. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347(2017)."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/CGO.2017.7863747"},{"key":"e_1_3_2_1_25_1","volume-title":"Policy gradient methods for reinforcement learning with function approximation. Advances in neural information processing systems 12","author":"Sutton S","year":"1999","unstructured":"Richard\u00a0S Sutton, David McAllester, Satinder Singh, and Yishay Mansour. 1999. Policy gradient methods for reinforcement learning with function approximation. Advances in neural information processing systems 12 (1999)."},{"volume-title":"Simulated annealing: Theory and applications","author":"Van\u00a0Laarhoven JM","key":"e_1_3_2_1_26_1","unstructured":"Peter\u00a0JM Van\u00a0Laarhoven and Emile\u00a0HL Aarts. 1987. Simulated annealing. In Simulated annealing: Theory and applications. Springer, 7\u201315."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1007\/11564096_42"},{"key":"e_1_3_2_1_28_1","volume-title":"14th USENIX Symposium on Operating Systems Design and Implementation (OSDI 20)","author":"Zheng Lianmin","year":"2020","unstructured":"Lianmin Zheng, Chengfan Jia, Minmin Sun, Zhao Wu, Cody\u00a0Hao Yu, Ameer Haj-Ali, Yida Wang, Jun Yang, Danyang Zhuo, Koushik Sen, 2020. Ansor: Generating {High-Performance} Tensor Programs for Deep Learning. In 14th USENIX Symposium on Operating Systems Design and Implementation (OSDI 20). 863\u2013879."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3373376.3378508"}],"event":{"name":"ICPP '22: 51st International Conference on Parallel Processing","acronym":"ICPP '22","location":"Bordeaux France"},"container-title":["Proceedings of the 51st International Conference on Parallel Processing"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3545008.3545020","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3545008.3545020","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:02:43Z","timestamp":1750186963000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3545008.3545020"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,29]]},"references-count":29,"alternative-id":["10.1145\/3545008.3545020","10.1145\/3545008"],"URL":"https:\/\/doi.org\/10.1145\/3545008.3545020","relation":{},"subject":[],"published":{"date-parts":[[2022,8,29]]},"assertion":[{"value":"2023-01-13","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}