• case-insensitive prefix search: default
  e.g., sig matches "SIGIR" as well as "signal"
• exact word search: append dollar sign ($) to word
  e.g., graph$ matches "graph", but not "graphics"
• boolean and: separate words by space
  e.g., codd model
• boolean or: connect words by pipe symbol (|)
  e.g., graph|network

Update May 7, 2017: Please note that we had to disable the phrase search operator (.) and the boolean not operator (-) due to technical problems. For the time being, phrase search queries will yield regular prefix search result, and search terms preceded by a minus will be interpreted as regular (positive) search terms.

found 70 matches

• 2024
• 
  K. Rameshkumar, Kaviarasu Nataraj, P. Krishnakumar, M. Saimurugan:
  Machine Learning Approach for Predicting the Solid Particle Lubricant Contamination in a Spherical Roller Bearing. IEEE Access 12: 78680-78700 (2024)
• 
  Anish Krishnakumar, Hanguang Yu, Tutu Ajayi, A. Alper Goksoy, Vishrut Pandey, Joshua Mack, Md Sahil Hassan, Kuan-Yu Chen, Chaitali Chakrabarti, Daniel W. Bliss, Ali Akoglu, Hun-Seok Kim, Ronald G. Dreslinski, David T. Blaauw, Ümit Y. Ogras:
  FALCON: An FPGA Emulation Platform for Domain-Specific SoCs (DSSoCs). IEEE Des. Test 41(1): 70-80 (2024)
• 
  Joonho Whangbo, Edwin Lim, Chengyi Lux Zhang, Kevin Anderson, Abraham Gonzalez, Raghav Gupta, Nivedha Krishnakumar, Sagar Karandikar, Borivoje Nikolic, Yakun Sophia Shao, Krste Asanovic:
  FireAxe: Partitioned FPGA-Accelerated Simulation of Large-Scale RTL Designs. ISCA 2024: 501-515
• 2023
• 
  Iain Barclay, Alun D. Preece, Ian J. Taylor, Swapna Krishnakumar Radha, Jarek Nabrzyski:
  Providing assurance and scrutability on shared data and machine learning models with verifiable credentials. Concurr. Comput. Pract. Exp. 35(18) (2023)
• 
  Radhakumari Maya, Muhammed Rasheed Irshad, Hassan S. Bakouch, Archana Krishnakumar, Najla Qarmalah:
  Kernel Estimation of the Extropy Function under α-Mixing Dependent Data. Symmetry 15(4): 796 (2023)
• 
  Nikhila T. Suresh, Vimina E. R., Krishnakumar U.:
  Topology Driven Analysis of Protein - Protein Interactome for Prioritizing Key Comorbid Genes via Sub Graph Based Average Path Length Centrality. IEEE ACM Trans. Comput. Biol. Bioinform. 20(1): 742-751 (2023)
• 
  Anish Krishnakumar, Ümit Y. Ogras, Radu Marculescu, Michael Kishinevsky, Trevor N. Mudge:
  Domain-Specific Architectures: Research Problems and Promising Approaches. ACM Trans. Embed. Comput. Syst. 22(2): 28:1-28:26 (2023)
• 
  Amin Firoozshahian, Joel Coburn, Roman Levenstein, Rakesh Nattoji, Ashwin Kamath, Olívia Wu, Gurdeepak Grewal, Harish Aepala, Bhasker Jakka, Bob Dreyer, Adam Hutchin, Utku Diril, Krishnakumar Nair, Ehsan K. Ardestani, Martin Schatz, Yuchen Hao, Rakesh Komuravelli, Kunming Ho, Sameer Abu Asal, Joe Shajrawi, Kevin Quinn, Nagesh Sreedhara, Pankaj Kansal, Willie Wei, Dheepak Jayaraman, Linda Cheng, Pritam Chopda, Eric Wang, Ajay Bikumandla, Arun Karthik Sengottuvel, Krishna Thottempudi, Ashwin Narasimha, Brian Dodds, Cao Gao, Jiyuan Zhang, Mohammed Al-Sanabani, Ana Zehtabioskuie, Jordan Fix, Hangchen Yu, Richard Li, Kaustubh Gondkar, Jack Montgomery, Mike Tsai, Saritha Dwarakapuram, Sanjay Desai, Nili Avidan, Poorvaja Ramani, Karthik Narayanan, Ajit Mathews, Sethu Gopal, Maxim Naumov, Vijay Rao, Krishna Noru, Harikrishna Reddy, Prahlad Venkatapuram, Alexis Bjorlin:
  MTIA: First Generation Silicon Targeting Meta's Recommendation Systems. ISCA 2023: 80:1-80:13
• 
  Hyoukjun Kwon, Krishnakumar Nair, Jamin Seo, Jason Yik, Debabrata Mohapatra, Dongyuan Zhan, Jinook Song, Peter Capak, Peizhao Zhang, Peter Vajda, Colby R. Banbury, Mark Mazumder, Liangzhen Lai, Ashish Sirasao, Tushar Krishna, Harshit Khaitan, Vikas Chandra, Vijay Janapa Reddi:
  XRBench: An Extended Reality (XR) Machine Learning Benchmark Suite for the Metaverse. MLSys 2023
• 
  Krishnakumar Balasubramanian, Larry Goldstein, Nathan Ross, Adil Salim:
  Gaussian random field approximation via Stein's method with applications to wide random neural networks. CoRR abs/2306.16308 (2023)
• 2022
• 
  A. Alper Goksoy, Anish Krishnakumar, Md Sahil Hassan, Allen-Jasmin Farcas, Ali Akoglu, Radu Marculescu, Ümit Y. Ogras:
  DAS: Dynamic Adaptive Scheduling for Energy-Efficient Heterogeneous SoCs. IEEE Embed. Syst. Lett. 14(1): 51-54 (2022)
• 
  Aishwarya Krishnakumar, Fathima Naushin A R, Mrithula K. L, B. Bharathi:
  Text summarization for Indian languages using pre-trained models. FIRE (Working Notes) 2022: 424-434
• 
  R. Shashidhar, Sanjay S. Tippannavar, Krishnakumar Balachandra Bhat, Neha Sharma, Mamoon Rashid, Arti Rana:
  EEG-based Brain Wave Recognition using GRU and LSTM. IC3I 2022: 1083-1087
• 
  Anish Krishnakumar, Radu Marculescu, Ümit Y. Ogras:
  INDENT: Incremental Online Decision Tree Training for Domain-Specific Systems-on-Chip. ICCAD 2022: 164:1-164:9
• 
  Ehsan K. Ardestani, Changkyu Kim, Seung Jae Lee, Luoshang Pan, Jens Axboe, Valmiki Rampersad, Banit Agrawal, Fuxun Yu, Ansha Yu, Trung Le, Hector Yuen, Dheevatsa Mudigere, Shishir Juluri, Akshat Nanda, Manoj Wodekar, Krishnakumar Nair, Maxim Naumov, Chris Petersen, Mikhail Smelyanskiy, Vijay Rao:
  Supporting Massive DLRM Inference through Software Defined Memory. ICDCS 2022: 302-312
• 
  Dheevatsa Mudigere, Yuchen Hao, Jianyu Huang, Zhihao Jia, Andrew Tulloch, Srinivas Sridharan, Xing Liu, Mustafa Ozdal, Jade Nie, Jongsoo Park, Liang Luo, Jie Amy Yang, Leon Gao, Dmytro Ivchenko, Aarti Basant, Yuxi Hu, Jiyan Yang, Ehsan K. Ardestani, Xiaodong Wang, Rakesh Komuravelli, Ching-Hsiang Chu, Serhat Yilmaz, Huayu Li, Jiyuan Qian, Zhuobo Feng, Yinbin Ma, Junjie Yang, Ellie Wen, Hong Li, Lin Yang, Chonglin Sun, Whitney Zhao, Dimitry Melts, Krishna Dhulipala, K. R. Kishore, Tyler Graf, Assaf Eisenman, Kiran Kumar Matam, Adi Gangidi, Guoqiang Jerry Chen, Manoj Krishnan, Avinash Nayak, Krishnakumar Nair, Bharath Muthiah, Mahmoud khorashadi, Pallab Bhattacharya, Petr Lapukhov, Maxim Naumov, Ajit Mathews, Lin Qiao, Mikhail Smelyanskiy, Bill Jia, Vijay Rao:
  Software-hardware co-design for fast and scalable training of deep learning recommendation models. ISCA 2022: 993-1011
• 
  Daniel W. Bliss, Tutu Ajayi, Ali Akoglu, Ilkin Aliyev, Toygun Basaklar, Leul Belayneh, David T. Blaauw, John S. Brunhaver, Chaitali Chakrabarti, Liangliang Chang, Kuan-Yu Chen, Ming-Hung Chen, Xing Chen, Alex R. Chiriyath, Alhad Daftardar, Ronald G. Dreslinski, Arindam Dutta, Allen-Jasmin Farcas, Y. Fu, A. Alper Goksoy, X. He, Md Sahil Hassan, Andrew Herschfelt, Jacob Holtom, Hun-Seok Kim, A. N. Krishnakumar, Y. Li, Owen Ma, Joshua Mack, Saurav Mallik, Sumit K. Mandal, Radu Marculescu, Brittany M. McCall, Trevor N. Mudge, Ümit Y. Ogras, Vishrut Pandey, Saquib Ahmad Siddiqui, Yu-Hsiu Sun, Adarsh A. Venkataramani, Xiangdong Wei, B. R. Willis, Hanguang Yu, Yufan Yue:
  Enabling Software-Defined RF Convergence with a Novel Coarse-Scale Heterogeneous Processor. ISCAS 2022: 443-447
• 
  Assaf Eisenman, Kiran Kumar Matam, Steven Ingram, Dheevatsa Mudigere, Raghuraman Krishnamoorthi, Krishnakumar Nair, Misha Smelyanskiy, Murali Annavaram:
  Check-N-Run: a Checkpointing System for Training Deep Learning Recommendation Models. NSDI 2022: 929-943
• 
  Benjamin Ghaemmaghami, Mustafa Ozdal, Rakesh Komuravelli, Dmitriy Korchev, Dheevatsa Mudigere, Krishnakumar Nair, Maxim Naumov:
  Learning to Collide: Recommendation System Model Compression with Learned Hash Functions. CoRR abs/2203.15837 (2022)
• 
  Kaiyuan Chen, Alexander Thomas, Hanming Lu, William Mullen, Jeffrey Ichnowski, Rahul Arya, Nivedha Krishnakumar, Ryan Teoh, Willis Wang, Anthony D. Joseph, John Kubiatowicz:
  SCL: A Secure Concurrency Layer For Paranoid Stateful Lambdas. CoRR abs/2210.11703 (2022)
• 
  Hyoukjun Kwon, Krishnakumar Nair, Jamin Seo, Jason Yik, Debabrata Mohapatra, Dongyuan Zhan, Jinook Song, Peter Capak, Peizhao Zhang, Peter Vajda, Colby R. Banbury, Mark Mazumder, Liangzhen Lai, Ashish Sirasao, Tushar Krishna, Harshit Khaitan, Vikas Chandra, Vijay Janapa Reddi:
  XRBench: An Extended Reality (XR) Machine Learning Benchmark Suite for the Metaverse. CoRR abs/2211.08675 (2022)
• 2021
• 
  Shouvanik Chakrabarti, Rajiv Krishnakumar, Guglielmo Mazzola, Nikitas Stamatopoulos, Stefan Woerner, William J. Zeng:
  A Threshold for Quantum Advantage in Derivative Pricing. Quantum 5: 463 (2021)
• 
  Juan López-Vinielles, José Antonio Fernandez Merodo, Pablo Ezquerro, Juan Carlos García-Davalillo, Roberto Sarro, Cristina Reyes-Carmona, Anna Barra, José A. Navarro, Vrinda Krishnakumar, Massimiliano Alvioli, Gerardo Herrera:
  Combining Satellite InSAR, Slope Units and Finite Element Modeling for Stability Analysis in Mining Waste Disposal Areas. Remote. Sens. 13(10): 2008 (2021)
• 
  Michael R. Smith, Stephen J. Verzi, Nicholas T. Johnson, Xin Zhou, Kanad Khanna, Sophie Quynn, Raga Krishnakumar:
  Malware Generation with Specific Behaviors to Improve Machine Learning-based Detection. IEEE BigData 2021: 2160-2169
• 
  Nicholas Roberts, Samuel Guo, Cong Xu, Ameet Talwalkar, David Lander, Lvfang Tao, Linhang Cai, Shuaicheng Niu, Jianyu Heng, Hongyang Qin, Minwen Deng, Johannes Hog, Alexander Pfefferle, Sushil Ammanaghatta Shivakumar, Arjun Krishnakumar, Yubo Wang, Rhea Sukthanker, Frank Hutter, Euxhen Hasanaj, Tien-Dung Le, Mikhail Khodak, Yuriy Nevmyvaka, Kashif Rasul, Frederic Sala, Anderson Schneider, Junhong Shen, Evan Randall Sparks:
  AutoML Decathlon: Diverse Tasks, Modern Methods, and Efficiency at Scale. NeurIPS (Competition and Demos) 2021: 151-170
• 
  Dheevatsa Mudigere, Yuchen Hao, Jianyu Huang, Andrew Tulloch, Srinivas Sridharan, Xing Liu, Mustafa Ozdal, Jade Nie, Jongsoo Park, Liang Luo, Jie Amy Yang, Leon Gao, Dmytro Ivchenko, Aarti Basant, Yuxi Hu, Jiyan Yang, Ehsan K. Ardestani, Xiaodong Wang, Rakesh Komuravelli, Ching-Hsiang Chu, Serhat Yilmaz, Huayu Li, Jiyuan Qian, Zhuobo Feng, Yinbin Ma, Junjie Yang, Ellie Wen, Hong Li, Lin Yang, Chonglin Sun, Whitney Zhao, Dimitry Melts, Krishna Dhulipala, K. R. Kishore, Tyler Graf, Assaf Eisenman, Kiran Kumar Matam, Adi Gangidi, Guoqiang Jerry Chen, Manoj Krishnan, Avinash Nayak, Krishnakumar Nair, Bharath Muthiah, Mahmoud khorashadi, Pallab Bhattacharya, Petr Lapukhov, Maxim Naumov, Lin Qiao, Mikhail Smelyanskiy, Bill Jia, Vijay Rao:
  High-performance, Distributed Training of Large-scale Deep Learning Recommendation Models. CoRR abs/2104.05158 (2021)
• 
  Iain Barclay, Alun D. Preece, Ian J. Taylor, Swapna Krishnakumar Radha, Jarek Nabrzyski:
  Providing Assurance and Scrutability on Shared Data and Machine Learning Models with Verifiable Credentials. CoRR abs/2105.06370 (2021)
• 
  A. Alper Goksoy, Anish Krishnakumar, Md Sahil Hassan, Allen-Jasmin Farcas, Ali Akoglu, Radu Marculescu, Ümit Y. Ogras:
  DAS: Dynamic Adaptive Scheduling for Energy-Efficient Heterogeneous SoCs. CoRR abs/2109.11069 (2021)
• 
  Ehsan K. Ardestani, Changkyu Kim, Seung Jae Lee, Luoshang Pan, Valmiki Rampersad, Jens Axboe, Banit Agrawal, Fuxun Yu, Ansha Yu, Trung Le, Hector Yuen, Shishir Juluri, Akshat Nanda, Manoj Wodekar, Dheevatsa Mudigere, Krishnakumar Nair, Maxim Naumov, Chris Peterson, Mikhail Smelyanskiy, Vijay Rao:
  Supporting Massive DLRM Inference Through Software Defined Memory. CoRR abs/2110.11489 (2021)
• 2020
• 
  Anderson L. Sartor, Anish Krishnakumar, Samet E. Arda, Ümit Y. Ogras, Radu Marculescu:
  HiLITE: Hierarchical and Lightweight Imitation Learning for Power Management of Embedded SoCs. IEEE Comput. Archit. Lett. 19(1): 63-67 (2020)

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