iBet uBet web content aggregator. Adding the entire web to your favor.
iBet uBet web content aggregator. Adding the entire web to your favor.



Link to original content: https://api.crossref.org/works/10.1145/3445814.3446762
{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T21:24:23Z","timestamp":1730323463720,"version":"3.28.0"},"publisher-location":"New York, NY, USA","reference-count":103,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,4,17]],"date-time":"2022-04-17T00:00:00Z","timestamp":1650153600000},"content-version":"vor","delay-in-days":365,"URL":"http:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"DARPA SDH","award":["HR0011-18-3-0007"]},{"name":"NSF","award":["942888"]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,4,19]]},"DOI":"10.1145\/3445814.3446762","type":"proceedings-article","created":{"date-parts":[[2021,4,11]],"date-time":"2021-04-11T17:06:26Z","timestamp":1618160786000},"page":"943-958","update-policy":"http:\/\/dx.doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":40,"title":["Mind mappings: enabling efficient algorithm-accelerator mapping space search"],"prefix":"10.1145","author":[{"given":"Kartik","family":"Hegde","sequence":"first","affiliation":[{"name":"University of Illinois at Urbana-Champaign, USA"}]},{"given":"Po-An","family":"Tsai","sequence":"additional","affiliation":[{"name":"NVIDIA, USA"}]},{"given":"Sitao","family":"Huang","sequence":"additional","affiliation":[{"name":"University of Illinois at Urbana-Champaign, USA"}]},{"given":"Vikas","family":"Chandra","sequence":"additional","affiliation":[{"name":"Facebook, USA"}]},{"given":"Angshuman","family":"Parashar","sequence":"additional","affiliation":[{"name":"NVIDIA, USA"}]},{"given":"Christopher W.","family":"Fletcher","sequence":"additional","affiliation":[{"name":"University of Illinois at Urbana-Champaign, USA"}]}],"member":"320","published-online":{"date-parts":[[2021,4,17]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"Mart\u00edn Abadi Ashish Agarwal Paul Barham Eugene Brevdo Zhifeng Chen Craig Citro Greg S. Corrado Andy Davis Jefrey Dean Matthieu Devin Sanjay Ghemawat Ian Goodfellow Andrew Harp Geofrey Irving Michael Isard Yangqing Jia Rafal Jozefowicz Lukasz Kaiser Manjunath Kudlur Josh Levenberg Dandelion Man\u00e9 Rajat Monga Sherry Moore Derek Murray Chris Olah Mike Schuster Jonathon Shlens Benoit Steiner Ilya Sutskever Kunal Talwar Paul Tucker Vincent Vanhoucke Vijay Vasudevan Fernanda Vi\u00e9gas Oriol Vinyals Pete Warden Martin Wattenberg Martin Wicke Yuan Yu and Xiaoqiang Zheng. 2015. TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. https:\/\/www.tensorflow.org\/ Software available from tensorflow. org. Mart\u00edn Abadi Ashish Agarwal Paul Barham Eugene Brevdo Zhifeng Chen Craig Citro Greg S. Corrado Andy Davis Jefrey Dean Matthieu Devin Sanjay Ghemawat Ian Goodfellow Andrew Harp Geofrey Irving Michael Isard Yangqing Jia Rafal Jozefowicz Lukasz Kaiser Manjunath Kudlur Josh Levenberg Dandelion Man\u00e9 Rajat Monga Sherry Moore Derek Murray Chris Olah Mike Schuster Jonathon Shlens Benoit Steiner Ilya Sutskever Kunal Talwar Paul Tucker Vincent Vanhoucke Vijay Vasudevan Fernanda Vi\u00e9gas Oriol Vinyals Pete Warden Martin Wattenberg Martin Wicke Yuan Yu and Xiaoqiang Zheng. 2015. TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. https:\/\/www.tensorflow.org\/ Software available from tensorflow. org."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3306346.3322967"},{"volume-title":"Reinforcement Learning and Adaptive Sampling for Optimized DNN Compilation. arXiv preprint arXiv","year":"1905","author":"Ahn Byung Hoon","key":"e_1_3_2_1_3_1","unstructured":"Byung Hoon Ahn , Prannoy Pilligundla , and Hadi Esmaeilzadeh . 2019. Reinforcement Learning and Adaptive Sampling for Optimized DNN Compilation. arXiv preprint arXiv : 1905 . 12799 ( 2019 ). Byung Hoon Ahn, Prannoy Pilligundla, and Hadi Esmaeilzadeh. 2019. Reinforcement Learning and Adaptive Sampling for Optimized DNN Compilation. arXiv preprint arXiv: 1905. 12799 ( 2019 )."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3007787.3001138"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/2628071.2628092"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.2514\/6.2000-4891"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/CGO.2019.8661197"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"crossref","unstructured":"Richard Bellman. 1957. A Markovian decision process. Journal of mathematics and mechanics ( 1957 ) 679-684. Richard Bellman. 1957. A Markovian decision process. Journal of mathematics and mechanics ( 1957 ) 679-684.","DOI":"10.1512\/iumj.1957.6.56038"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"crossref","unstructured":"Eliot Bolduc George C Knee Erik M Gauger and Jonathan Leach. 2017. Projected gradient descent algorithms for quantum state tomography. npj Quantum Information 3 1 ( 2017 ) 1-9. Eliot Bolduc George C Knee Erik M Gauger and Jonathan Leach. 2017. Projected gradient descent algorithms for quantum state tomography. npj Quantum Information 3 1 ( 2017 ) 1-9.","DOI":"10.1038\/s41534-017-0043-1"},{"key":"e_1_3_2_1_10_1","unstructured":"Justin A Boyan and Andrew W Moore. 1995. Generalization in reinforcement learning: Safely approximating the value function. In Advances in neural information processing systems. 369-376. Justin A Boyan and Andrew W Moore. 1995. Generalization in reinforcement learning: Safely approximating the value function. In Advances in neural information processing systems. 369-376."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"crossref","unstructured":"J Douglas Carroll and Jih-Jie Chang. 1970. Analysis of individual diferences in multidimensional scaling via an N-way generalization of ?Eckart-Young? decomposition. Psychometrika 35 3 ( 1970 ) 283-319. J Douglas Carroll and Jih-Jie Chang. 1970. Analysis of individual diferences in multidimensional scaling via an N-way generalization of ?Eckart-Young? decomposition. Psychometrika 35 3 ( 1970 ) 283-319.","DOI":"10.1007\/BF02310791"},{"volume-title":"MARVEL: A Decoupled Model-driven Approach for Eficiently Mapping Convolutions on Spatial DNN Accelerators. arXiv preprint arXiv","year":"2020","author":"Chatarasi Prasanth","key":"e_1_3_2_1_12_1","unstructured":"Prasanth Chatarasi , Hyoukjun Kwon , Natesh Raina , Saurabh Malik , Vaisakh Haridas , Tushar Krishna , and Vivek Sarkar . 2020 . MARVEL: A Decoupled Model-driven Approach for Eficiently Mapping Convolutions on Spatial DNN Accelerators. arXiv preprint arXiv : 2002. 07752 ( 2020 ). Prasanth Chatarasi, Hyoukjun Kwon, Natesh Raina, Saurabh Malik, Vaisakh Haridas, Tushar Krishna, and Vivek Sarkar. 2020. MARVEL: A Decoupled Model-driven Approach for Eficiently Mapping Convolutions on Spatial DNN Accelerators. arXiv preprint arXiv: 2002. 07752 ( 2020 )."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"crossref","unstructured":"Stephen Chen James Montgomery and Antonio Boluf\u00e9-R\u00f6hler. 2015. Measuring the curse of dimensionality and its efects on particle swarm optimization and diferential evolution. Applied Intelligence 42 3 ( 2015 ) 514-526. Stephen Chen James Montgomery and Antonio Boluf\u00e9-R\u00f6hler. 2015. Measuring the curse of dimensionality and its efects on particle swarm optimization and diferential evolution. Applied Intelligence 42 3 ( 2015 ) 514-526.","DOI":"10.1007\/s10489-014-0613-2"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"e_1_3_2_1_15_1","first-page":"578","volume-title":"13th USENIX Symposium on Operating Systems Design and Implementation (OSDI 18)","author":"Chen Tianqi","year":"2018","unstructured":"Tianqi Chen , Thierry Moreau , Ziheng Jiang , Lianmin Zheng , Eddie Yan , Haichen Shen , Meghan Cowan , Leyuan Wang , Yuwei Hu , Luis Ceze , 2018 . TVM: An automated end-to-end optimizing compiler for deep learning . In 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI 18) . 578 - 594 . Tianqi Chen, Thierry Moreau, Ziheng Jiang, Lianmin Zheng, Eddie Yan, Haichen Shen, Meghan Cowan, Leyuan Wang, Yuwei Hu, Luis Ceze, et al. 2018. TVM: An automated end-to-end optimizing compiler for deep learning. In 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI 18). 578-594."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"crossref","unstructured":"Tianshi Chen Ke Tang Guoliang Chen and Xin Yao. 2012. A large population size can be unhelpful in evolutionary algorithms. Theoretical Computer Science 436 ( 2012 ) 54-70. Tianshi Chen Ke Tang Guoliang Chen and Xin Yao. 2012. A large population size can be unhelpful in evolutionary algorithms. Theoretical Computer Science 436 ( 2012 ) 54-70.","DOI":"10.1016\/j.tcs.2011.02.016"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00601"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/MICRO.2014.58"},{"key":"e_1_3_2_1_19_1","unstructured":"Yudong Chen and Martin J Wainwright. 2015. Fast low-rank estimation by projected gradient descent: General statistical and algorithmic guarantees. arXiv preprint arXiv:1509.03025 ( 2015 ). Yudong Chen and Martin J Wainwright. 2015. Fast low-rank estimation by projected gradient descent: General statistical and algorithmic guarantees. arXiv preprint arXiv:1509.03025 ( 2015 )."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3007787.3001177"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/JETCAS.2019.2910232"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/IEMBS.2006.260286"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/IEMBS.2006.260286"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3358198"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/2749469.2750389"},{"key":"e_1_3_2_1_27_1","first-page":"508","article-title":"Hardware accelerator for elliptic curve cryptography","volume":"7","author":"Eberle Hans","year":"2009","unstructured":"Hans Eberle , Nils Gura , Daniel Finchelstein , Sheueling Chang-Shantz , and Vipul Gupta . 2009 . Hardware accelerator for elliptic curve cryptography . US Patent 7 , 508 , 936. Hans Eberle, Nils Gura, Daniel Finchelstein, Sheueling Chang-Shantz, and Vipul Gupta. 2009. Hardware accelerator for elliptic curve cryptography. US Patent 7, 508, 936.","journal-title":"US Patent"},{"volume-title":"Marc-Andr\u00e9 Gardner, Marc Parizeau, and Christian Gagn\u00e9.","year":"2012","author":"Fortin F\u00e9lix-Antoine","key":"e_1_3_2_1_28_1","unstructured":"F\u00e9lix-Antoine Fortin , Fran\u00e7ois-Michel De Rainville , Marc-Andr\u00e9 Gardner, Marc Parizeau, and Christian Gagn\u00e9. 2012 . DEAP : Evolutionary Algorithms Made Easy. Journal of Machine Learning Research 13 (july 2012 ), 2171-2175. F\u00e9lix-Antoine Fortin, Fran\u00e7ois-Michel De Rainville, Marc-Andr\u00e9 Gardner, Marc Parizeau, and Christian Gagn\u00e9. 2012. DEAP: Evolutionary Algorithms Made Easy. Journal of Machine Learning Research 13 (july 2012 ), 2171-2175."},{"key":"e_1_3_2_1_29_1","unstructured":"David E Goldberg. 2006. Genetic algorithms. Pearson Education India. David E Goldberg. 2006. Genetic algorithms. Pearson Education India."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098043"},{"key":"e_1_3_2_1_31_1","unstructured":"Will Grathwohl Dami Choi Yuhuai Wu Geofrey Roeder and David Duvenaud. 2017. Backpropagation through the void: Optimizing control variates for blackbox gradient estimation. arXiv preprint arXiv:1711.00123 ( 2017 ). Will Grathwohl Dami Choi Yuhuai Wu Geofrey Roeder and David Duvenaud. 2017. Backpropagation through the void: Optimizing control variates for blackbox gradient estimation. arXiv preprint arXiv:1711.00123 ( 2017 )."},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3007787.3001163"},{"key":"e_1_3_2_1_33_1","unstructured":"Song Han Huizi Mao and William J Dally. 2015. Deep compression: Compressing deep neural networks with pruning trained quantization and hufman coding. arXiv preprint arXiv:1510.00149 ( 2015 ). Song Han Huizi Mao and William J Dally. 2015. Deep compression: Compressing deep neural networks with pruning trained quantization and hufman coding. arXiv preprint arXiv:1510.00149 ( 2015 )."},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"crossref","unstructured":"Ahmad Hassanat Khalid Almohammadi Esra' Alkafaween Eman Abunawas Awni Hammouri and VB Prasath. 2019. Choosing Mutation and Crossover Ratios for Genetic Algorithms-A Review with a New Dynamic Approach. Information 10 12 ( 2019 ) 390. Ahmad Hassanat Khalid Almohammadi Esra' Alkafaween Eman Abunawas Awni Hammouri and VB Prasath. 2019. Choosing Mutation and Crossover Ratios for Genetic Algorithms-A Review with a New Dynamic Approach. Information 10 12 ( 2019 ) 390.","DOI":"10.3390\/info10120390"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/MICRO.2018.00080"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3352460.3358275"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISCA.2018.00062"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"crossref","unstructured":"John Henry Holland etal 1992. Adaptation in natural and artificial systems: an introductory analysis with applications to biology control and artificial intelligence. MIT press. John Henry Holland et al. 1992. Adaptation in natural and artificial systems: an introductory analysis with applications to biology control and artificial intelligence. MIT press.","DOI":"10.7551\/mitpress\/1090.001.0001"},{"volume-title":"Breakthroughs in statistics","author":"Huber Peter J","key":"e_1_3_2_1_40_1","unstructured":"Peter J Huber . 1992. Robust estimation of a location parameter . In Breakthroughs in statistics . Springer , 492-518. Peter J Huber. 1992. Robust estimation of a location parameter. In Breakthroughs in statistics. Springer, 492-518."},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/BMEI.2015.7401470"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/1168917.1168882"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/3079856.3080246"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/3400302.3415639"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"crossref","unstructured":"Scott Kirkpatrick C Daniel Gelatt and Mario P Vecchi. 1983. Optimization by simulated annealing. science 220 4598 ( 1983 ) 671-680. Scott Kirkpatrick C Daniel Gelatt and Mario P Vecchi. 1983. Optimization by simulated annealing. science 220 4598 ( 1983 ) 671-680.","DOI":"10.1126\/science.220.4598.671"},{"volume-title":"An alternative view: When does SGD escape local minima? arXiv preprint arXiv","year":"1802","author":"Kleinberg Robert","key":"e_1_3_2_1_46_1","unstructured":"Robert Kleinberg , Yuanzhi Li , and Yang Yuan . 2018. An alternative view: When does SGD escape local minima? arXiv preprint arXiv : 1802 . 06175 ( 2018 ). Robert Kleinberg, Yuanzhi Li, and Yang Yuan. 2018. An alternative view: When does SGD escape local minima? arXiv preprint arXiv: 1802. 06175 ( 2018 )."},{"volume-title":"Tensor decompositions and applications","series-title":"SIAM review 51, 3 ( 2009 ), 455-500","author":"Kolda Tamara G","key":"e_1_3_2_1_47_1","unstructured":"Tamara G Kolda and Brett W Bader . 2009. Tensor decompositions and applications . SIAM review 51, 3 ( 2009 ), 455-500 . Tamara G Kolda and Brett W Bader. 2009. Tensor decompositions and applications. SIAM review 51, 3 ( 2009 ), 455-500."},{"key":"e_1_3_2_1_48_1","unstructured":"Vijay R Konda and John N Tsitsiklis. 2000. Actor-critic algorithms. In Advances in neural information processing systems. 1008-1014. Vijay R Konda and John N Tsitsiklis. 2000. Actor-critic algorithms. In Advances in neural information processing systems. 1008-1014."},{"volume-title":"Surrogate-based modeling and optimization","author":"Koziel Slawomir","key":"e_1_3_2_1_49_1","unstructured":"Slawomir Koziel and Leifur Leifsson . 2013. Surrogate-based modeling and optimization . Springer . Slawomir Koziel and Leifur Leifsson. 2013. Surrogate-based modeling and optimization. Springer."},{"key":"e_1_3_2_1_50_1","unstructured":"Alex Krizhevsky Ilya Sutskever and Geofrey E Hinton. 2012. Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 ( 2012 ) 1097-1105. Alex Krizhevsky Ilya Sutskever and Geofrey E Hinton. 2012. Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 ( 2012 ) 1097-1105."},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1145\/3352460.3358252"},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1145\/3296957.3173176"},{"key":"e_1_3_2_1_53_1","first-page":"21","volume-title":"Proceedings of the 1988 connectionist models summer school","volume":"1","author":"LeCun Yann","year":"1988","unstructured":"Yann LeCun , D Touresky , G Hinton , and T Sejnowski . 1988 . A theoretical framework for back-propagation . In Proceedings of the 1988 connectionist models summer school , Vol. 1 . CMU, Pittsburgh, Pa : Morgan Kaufmann, 21 - 28 . Yann LeCun, D Touresky, G Hinton, and T Sejnowski. 1988. A theoretical framework for back-propagation. In Proceedings of the 1988 connectionist models summer school, Vol. 1. CMU, Pittsburgh, Pa: Morgan Kaufmann, 21-28."},{"key":"e_1_3_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-35289-8_3"},{"key":"e_1_3_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1109\/HPCA.2007.346211"},{"volume-title":"4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, May 2-4, 2016, Conference Track Proceedings. http:\/\/arxiv.org\/abs\/1509","year":"2016","author":"Lillicrap Timothy P.","key":"e_1_3_2_1_56_1","unstructured":"Timothy P. Lillicrap , Jonathan J. Hunt , Alexander Pritzel , Nicolas Heess , Tom Erez , Yuval Tassa , David Silver , and Daan Wierstra . 2016 . Continuous control with deep reinforcement learning . In 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, May 2-4, 2016, Conference Track Proceedings. http:\/\/arxiv.org\/abs\/1509 .02971 Timothy P. Lillicrap, Jonathan J. Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra. 2016. Continuous control with deep reinforcement learning. In 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, May 2-4, 2016, Conference Track Proceedings. http:\/\/arxiv.org\/abs\/1509.02971"},{"volume-title":"Darts: Diferentiable architecture search. arXiv preprint arXiv","year":"2018","author":"Liu Hanxiao","key":"e_1_3_2_1_57_1","unstructured":"Hanxiao Liu , Karen Simonyan , and Yiming Yang . 2018 . Darts: Diferentiable architecture search. arXiv preprint arXiv : 1806. 09055 ( 2018 ). Hanxiao Liu, Karen Simonyan, and Yiming Yang. 2018. Darts: Diferentiable architecture search. arXiv preprint arXiv: 1806. 09055 ( 2018 )."},{"key":"e_1_3_2_1_58_1","unstructured":"Gilles Louppe Joeri Hermans and Kyle Cranmer. 2017. Adversarial variational optimization of non-diferentiable simulators. arXiv preprint arXiv:1707.07113 ( 2017 ). Gilles Louppe Joeri Hermans and Kyle Cranmer. 2017. Adversarial variational optimization of non-diferentiable simulators. arXiv preprint arXiv:1707.07113 ( 2017 )."},{"key":"e_1_3_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.1109\/HPCA.2017.29"},{"key":"e_1_3_2_1_60_1","unstructured":"Chris J Maddison Andriy Mnih and Yee Whye Teh. 2016. The concrete distribution: A continuous relaxation of discrete random variables. arXiv preprint arXiv:1611.00712 ( 2016 ). Chris J Maddison Andriy Mnih and Yee Whye Teh. 2016. The concrete distribution: A continuous relaxation of discrete random variables. arXiv preprint arXiv:1611.00712 ( 2016 )."},{"key":"e_1_3_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSSC.2014.2384039"},{"volume-title":"Ithemal: Accurate, portable and fast basic block throughput estimation using deep neural networks. arXiv preprint arXiv:1808. 07412 ( 2018 ).","year":"2018","author":"Mendis Charith","key":"e_1_3_2_1_62_1","unstructured":"Charith Mendis , Alex Renda , Saman Amarasinghe , and Michael Carbin . 2018 . Ithemal: Accurate, portable and fast basic block throughput estimation using deep neural networks. arXiv preprint arXiv:1808. 07412 ( 2018 ). Charith Mendis, Alex Renda, Saman Amarasinghe, and Michael Carbin. 2018. Ithemal: Accurate, portable and fast basic block throughput estimation using deep neural networks. arXiv preprint arXiv:1808. 07412 ( 2018 )."},{"key":"e_1_3_2_1_63_1","unstructured":"Tomas Mikolov Kai Chen Gregory S. Corrado and Jefrey Dean. 2013. Eficient Estimation of Word Representations in Vector Space. CoRR abs\/1301.3781 ( 2013 ). Tomas Mikolov Kai Chen Gregory S. Corrado and Jefrey Dean. 2013. Eficient Estimation of Word Representations in Vector Space. CoRR abs\/1301.3781 ( 2013 )."},{"key":"e_1_3_2_1_64_1","article-title":"Optimal routing in ad-hoc network using genetic algorithm","volume":"3","author":"Mohammed Anjum A","year":"2012","unstructured":"Anjum A Mohammed and Gihan Nagib . 2012 . Optimal routing in ad-hoc network using genetic algorithm . Int. J. Advanced Networking and Applications 3 , 05 ( 2012 ), 1323-1328. Anjum A Mohammed and Gihan Nagib. 2012. Optimal routing in ad-hoc network using genetic algorithm. Int. J. Advanced Networking and Applications 3, 05 ( 2012 ), 1323-1328.","journal-title":"Int. J. Advanced Networking and Applications"},{"volume-title":"Introductory lectures on convex optimization: A basic course","author":"Nesterov Yurii","key":"e_1_3_2_1_65_1","unstructured":"Yurii Nesterov . 2013. Introductory lectures on convex optimization: A basic course . Vol. 87 . Springer Science & Business Media . Yurii Nesterov. 2013. Introductory lectures on convex optimization: A basic course. Vol. 87. Springer Science & Business Media."},{"key":"e_1_3_2_1_66_1","unstructured":"NVIDIA. [n.d.]. The NVIDIA Deep Learning Accelerator (NVDLA). http:\/\/nvdla.org\/hw\/v1\/ias\/programming_guide.html. NVIDIA. [n.d.]. The NVIDIA Deep Learning Accelerator (NVDLA). http:\/\/nvdla.org\/hw\/v1\/ias\/programming_guide.html."},{"key":"e_1_3_2_1_67_1","doi-asserted-by":"crossref","unstructured":"Hari Mohan Pandey Ankit Chaudhary and Deepti Mehrotra. 2014. A comparative review of approaches to prevent premature convergence in GA. Applied Soft Computing 24 ( 2014 ) 1047-1077. Hari Mohan Pandey Ankit Chaudhary and Deepti Mehrotra. 2014. A comparative review of approaches to prevent premature convergence in GA. Applied Soft Computing 24 ( 2014 ) 1047-1077.","DOI":"10.1016\/j.asoc.2014.08.025"},{"key":"e_1_3_2_1_68_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISPASS.2019.00042"},{"key":"e_1_3_2_1_69_1","doi-asserted-by":"publisher","DOI":"10.1145\/3079856.3080254"},{"key":"e_1_3_2_1_70_1","unstructured":"Razvan Pascanu Tomas Mikolov and Yoshua Bengio. 2012. Understanding the exploding gradient problem. CoRR abs\/1211.5063 2 ( 2012 ). Razvan Pascanu Tomas Mikolov and Yoshua Bengio. 2012. Understanding the exploding gradient problem. CoRR abs\/1211.5063 2 ( 2012 )."},{"key":"e_1_3_2_1_71_1","first-page":"8024","article-title":"PyTorch: An imperative style, high-performance deep learning library","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 . In Advances in Neural Information Processing Systems. 8024 - 8035 . Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. PyTorch: An imperative style, high-performance deep learning library. In Advances in Neural Information Processing Systems. 8024-8035.","journal-title":"Advances in Neural Information Processing Systems."},{"key":"e_1_3_2_1_72_1","first-page":"193","article-title":"CLITE: Eficient and QoS-Aware CoLocation of Multiple Latency-Critical Jobs for Warehouse Scale Computers. In 2020 IEEE International Symposium on High Performance Computer Architecture (HPCA)","author":"Patel Tirthak","year":"2020","unstructured":"Tirthak Patel and Devesh Tiwari . 2020 . CLITE: Eficient and QoS-Aware CoLocation of Multiple Latency-Critical Jobs for Warehouse Scale Computers. In 2020 IEEE International Symposium on High Performance Computer Architecture (HPCA) . IEEE , 193 - 206 . Tirthak Patel and Devesh Tiwari. 2020. CLITE: Eficient and QoS-Aware CoLocation of Multiple Latency-Critical Jobs for Warehouse Scale Computers. In 2020 IEEE International Symposium on High Performance Computer Architecture (HPCA). IEEE, 193-206.","journal-title":"IEEE"},{"key":"e_1_3_2_1_73_1","doi-asserted-by":"publisher","DOI":"10.1145\/3297858.3304025"},{"key":"e_1_3_2_1_74_1","unstructured":"Matthew Perry. 2019. Python module for simulated annealing. https:\/\/github. com\/perrygeo\/simanneal. Matthew Perry. 2019. Python module for simulated annealing. https:\/\/github. com\/perrygeo\/simanneal."},{"key":"e_1_3_2_1_75_1","doi-asserted-by":"crossref","unstructured":"Nestor V Queipo Raphael T Haftka Wei Shyy Tushar Goel Rajkumar Vaidyanathan and P Kevin Tucker. 2005. Surrogate-based analysis and optimization. Progress in aerospace sciences ( 2005 ). Nestor V Queipo Raphael T Haftka Wei Shyy Tushar Goel Rajkumar Vaidyanathan and P Kevin Tucker. 2005. Surrogate-based analysis and optimization. Progress in aerospace sciences ( 2005 ).","DOI":"10.1016\/j.paerosci.2005.02.001"},{"key":"e_1_3_2_1_76_1","doi-asserted-by":"publisher","DOI":"10.1145\/2499370.2462176"},{"key":"e_1_3_2_1_77_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISLPED.2017.8009208"},{"volume-title":"Dif Tune: Optimizing CPU Simulator Parameters with Learned Diferentiable Surrogates. In 2020 53rd Annual IEEE\/ACM International Symposium on Microarchitecture (MICRO). IEEE.","year":"2020","author":"Renda Alex","key":"e_1_3_2_1_78_1","unstructured":"Alex Renda , Yishen Chen , Charith Mendis , and Michael Carbin . 2020 . Dif Tune: Optimizing CPU Simulator Parameters with Learned Diferentiable Surrogates. In 2020 53rd Annual IEEE\/ACM International Symposium on Microarchitecture (MICRO). IEEE. Alex Renda, Yishen Chen, Charith Mendis, and Michael Carbin. 2020. Dif Tune: Optimizing CPU Simulator Parameters with Learned Diferentiable Surrogates. In 2020 53rd Annual IEEE\/ACM International Symposium on Microarchitecture (MICRO). IEEE."},{"key":"e_1_3_2_1_79_1","first-page":"3047","article-title":"Constructing deep neural networks by Bayesian network structure learning","author":"Rohekar Raanan Y","year":"2018","unstructured":"Raanan Y Rohekar , Shami Nisimov , Yaniv Gurwicz , Guy Koren , and Gal Novik . 2018 . Constructing deep neural networks by Bayesian network structure learning . In Advances in Neural Information Processing Systems. 3047 - 3058 . Raanan Y Rohekar, Shami Nisimov, Yaniv Gurwicz, Guy Koren, and Gal Novik. 2018. Constructing deep neural networks by Bayesian network structure learning. In Advances in Neural Information Processing Systems. 3047-3058.","journal-title":"Advances in Neural Information Processing Systems."},{"volume-title":"Scale-sim: Systolic cnn accelerator. arXiv preprint arXiv","year":"2018","author":"Samajdar Ananda","key":"e_1_3_2_1_80_1","unstructured":"Ananda Samajdar , Yuhao Zhu , Paul Whatmough , Matthew Mattina , and Tushar Krishna . 2018 . Scale-sim: Systolic cnn accelerator. arXiv preprint arXiv : 1811. 02883 ( 2018 ). Ananda Samajdar, Yuhao Zhu, Paul Whatmough, Matthew Mattina, and Tushar Krishna. 2018. Scale-sim: Systolic cnn accelerator. arXiv preprint arXiv: 1811. 02883 ( 2018 )."},{"volume-title":"Workshop on Real World Experiment Design and Active Learning at International Conference on Machine Learning.","year":"2020","author":"Shirobokov Sergey","key":"e_1_3_2_1_81_1","unstructured":"Sergey Shirobokov , Vladislav Belavin , Michael Kagan , Andrei Ustyuzhanin , and Atilim Gunes Baydin . 2020 . Black-box optimization with local generative surrogates . In Workshop on Real World Experiment Design and Active Learning at International Conference on Machine Learning. Sergey Shirobokov, Vladislav Belavin, Michael Kagan, Andrei Ustyuzhanin, and Atilim Gunes Baydin. 2020. Black-box optimization with local generative surrogates. In Workshop on Real World Experiment Design and Active Learning at International Conference on Machine Learning."},{"key":"e_1_3_2_1_82_1","unstructured":"Karen Simonyan and Andrew Zisserman. 2014. Very Deep Convolutional Networks for Large-Scale Image Recognition. CoRR abs\/1409.1556 ( 2014 ). http:\/\/arxiv.org\/abs\/1409.1556 Karen Simonyan and Andrew Zisserman. 2014. Very Deep Convolutional Networks for Large-Scale Image Recognition. CoRR abs\/1409.1556 ( 2014 ). http:\/\/arxiv.org\/abs\/1409.1556"},{"volume-title":"Multi-way analysis: applications in the chemical sciences","author":"Smilde Age","key":"e_1_3_2_1_83_1","unstructured":"Age Smilde , Rasmus Bro , and Paul Geladi . 2005. Multi-way analysis: applications in the chemical sciences . John Wiley & Sons . Age Smilde, Rasmus Bro, and Paul Geladi. 2005. Multi-way analysis: applications in the chemical sciences. John Wiley & Sons."},{"key":"e_1_3_2_1_84_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-12239-2_56"},{"key":"e_1_3_2_1_85_1","doi-asserted-by":"publisher","DOI":"10.1109\/IPDPS.2017.84"},{"volume-title":"Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research 15, 1 ( 2014 )","year":"1929","author":"Srivastava Nitish","key":"e_1_3_2_1_86_1","unstructured":"Nitish Srivastava , Geofrey Hinton , Alex Krizhevsky , Ilya Sutskever , and Ruslan Salakhutdinov . 2014. Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research 15, 1 ( 2014 ) , 1929 -1958. Nitish Srivastava, Geofrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014. Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research 15, 1 ( 2014 ), 1929-1958."},{"key":"e_1_3_2_1_87_1","doi-asserted-by":"publisher","DOI":"10.1109\/HPCA47549.2020.00062"},{"first-page":"87","volume-title":"International Journal of software Engineering and its Applications 3, 4 ( 2009 )","author":"Srivastava Praveen Ranjan","key":"e_1_3_2_1_88_1","unstructured":"Praveen Ranjan Srivastava and Tai-hoon Kim. 2009. Application of genetic algorithm in software testing . International Journal of software Engineering and its Applications 3, 4 ( 2009 ) , 87 - 96 . Praveen Ranjan Srivastava and Tai-hoon Kim. 2009. Application of genetic algorithm in software testing. International Journal of software Engineering and its Applications 3, 4 ( 2009 ), 87-96."},{"key":"e_1_3_2_1_89_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1008202821328"},{"key":"e_1_3_2_1_90_1","unstructured":"Richard S Sutton David A McAllester Satinder P Singh and Yishay Mansour. 2000. Policy gradient methods for reinforcement learning with function approximation. In Advances in neural information processing systems. 1057-1063. Richard S Sutton David A McAllester Satinder P Singh and Yishay Mansour. 2000. Policy gradient methods for reinforcement learning with function approximation. In Advances in neural information processing systems. 1057-1063."},{"key":"e_1_3_2_1_91_1","first-page":"1057","volume-title":"NIPs","volume":"99","author":"Sutton Richard S","year":"1999","unstructured":"Richard S Sutton , David A McAllester , Satinder P Singh , Yishay Mansour , 1999 . Policy gradient methods for reinforcement learning with function approximation .. In NIPs , Vol. 99 . Citeseer , 1057 - 1063 . Richard S Sutton, David A McAllester, Satinder P Singh, Yishay Mansour, et al. 1999. Policy gradient methods for reinforcement learning with function approximation.. In NIPs, Vol. 99. Citeseer, 1057-1063."},{"key":"e_1_3_2_1_92_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"e_1_3_2_1_93_1","unstructured":"G Tomasi. 2005. Use of the properties of the Khatri-Rao product for the computation of Jacobian. Hessian and gradient of the PARAFAC model under MATLAB ( 2005 ). G Tomasi. 2005. Use of the properties of the Khatri-Rao product for the computation of Jacobian. Hessian and gradient of the PARAFAC model under MATLAB ( 2005 )."},{"volume-title":"Derek Nowrouzezahrai, Jean-Fran\u00e7ois Lalonde, and Felix Heide.","year":"2019","author":"Tseng Ethan","key":"e_1_3_2_1_94_1","unstructured":"Ethan Tseng , Felix Yu , Yuting Yang , Fahim Mannan , Karl ST Arnaud , Derek Nowrouzezahrai, Jean-Fran\u00e7ois Lalonde, and Felix Heide. 2019 . Hyperparameter optimization in black-box image processing using diferentiable proxies. ACM Transactions on Graphics ( 2019 ). Ethan Tseng, Felix Yu, Yuting Yang, Fahim Mannan, Karl ST Arnaud, Derek Nowrouzezahrai, Jean-Fran\u00e7ois Lalonde, and Felix Heide. 2019. Hyperparameter optimization in black-box image processing using diferentiable proxies. ACM Transactions on Graphics ( 2019 )."},{"key":"e_1_3_2_1_95_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVLSI.2017.2688340"},{"volume-title":"Rebar: Low-variance, unbiased gradient estimates for discrete latent variable models. arXiv preprint arXiv:1703.07370 ( 2017 ).","year":"2017","author":"Tucker George","key":"e_1_3_2_1_96_1","unstructured":"George Tucker , Andriy Mnih , Chris J Maddison , Dieterich Lawson , and Jascha Sohl-Dickstein . 2017 . Rebar: Low-variance, unbiased gradient estimates for discrete latent variable models. arXiv preprint arXiv:1703.07370 ( 2017 ). George Tucker, Andriy Mnih, Chris J Maddison, Dieterich Lawson, and Jascha Sohl-Dickstein. 2017. Rebar: Low-variance, unbiased gradient estimates for discrete latent variable models. arXiv preprint arXiv:1703.07370 ( 2017 )."},{"volume-title":"Tensor comprehensions: Framework-agnostic high-performance machine learning abstractions. arXiv preprint arXiv","year":"1802","author":"Vasilache Nicolas","key":"e_1_3_2_1_97_1","unstructured":"Nicolas Vasilache , Oleksandr Zinenko , Theodoros Theodoridis , Priya Goyal , Zachary DeVito , William S Moses , Sven Verdoolaege , Andrew Adams , and Albert Cohen . 2018. Tensor comprehensions: Framework-agnostic high-performance machine learning abstractions. arXiv preprint arXiv : 1802 . 04730 ( 2018 ). Nicolas Vasilache, Oleksandr Zinenko, Theodoros Theodoridis, Priya Goyal, Zachary DeVito, William S Moses, Sven Verdoolaege, Andrew Adams, and Albert Cohen. 2018. Tensor comprehensions: Framework-agnostic high-performance machine learning abstractions. arXiv preprint arXiv: 1802. 04730 ( 2018 )."},{"key":"e_1_3_2_1_98_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00881"},{"key":"e_1_3_2_1_99_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01099"},{"key":"e_1_3_2_1_100_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISCA.2018.00060"},{"key":"e_1_3_2_1_101_1","doi-asserted-by":"publisher","DOI":"10.1145\/3140659.3080215"},{"key":"e_1_3_2_1_102_1","doi-asserted-by":"publisher","DOI":"10.1145\/3373376.3378508"},{"key":"e_1_3_2_1_103_1","unstructured":"Barret Zoph and Quoc V Le. 2016. Neural architecture search with reinforcement learning. arXiv preprint arXiv:1611.01578 ( 2016 ). Barret Zoph and Quoc V Le. 2016. Neural architecture search with reinforcement learning. arXiv preprint arXiv:1611.01578 ( 2016 )."}],"event":{"name":"ASPLOS '21: 26th ACM International Conference on Architectural Support for Programming Languages and Operating Systems","sponsor":["SIGPLAN ACM Special Interest Group on Programming Languages"],"location":"Virtual USA","acronym":"ASPLOS '21"},"container-title":["Proceedings of the 26th ACM International Conference on Architectural Support for Programming Languages and Operating Systems"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3445814.3446762","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3445814.3446762","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,2]],"date-time":"2023-05-02T04:31:07Z","timestamp":1683001867000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3445814.3446762"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,17]]},"references-count":103,"alternative-id":["10.1145\/3445814.3446762","10.1145\/3445814"],"URL":"https:\/\/doi.org\/10.1145\/3445814.3446762","relation":{},"subject":[],"published":{"date-parts":[[2021,4,17]]},"assertion":[{"value":"2021-04-17","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}