Search Results - "Diamos, Greg"

MLPerf: An Industry Standard Benchmark Suite for Machine Learning Performance by Mattson, Peter, Reddi, Vijay Janapa, Cheng, Christine, Coleman, Cody, Diamos, Greg, Kanter, David, Micikevicius, Paulius, Patterson, David, Schmuelling, Guenther, Tang, Hanlin, Wei, Gu-Yeon, Wu, Carole-Jean

“…In this article, we describe the design choices behind MLPerf, a machine learning performance benchmark that has become an industry standard. The first two…”
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MLPerf Inference Benchmark by Reddi, Vijay Janapa, Cheng, Christine, Kanter, David, Mattson, Peter, Schmuelling, Guenther, Wu, Carole-Jean, Anderson, Brian, Breughe, Maximilien, Charlebois, Mark, Chou, William, Chukka, Ramesh, Coleman, Cody, Davis, Sam, Deng, Pan, Diamos, Greg, Duke, Jared, Fick, Dave, Gardner, J. Scott, Hubara, Itay, Idgunji, Sachin, Jablin, Thomas B., Jiao, Jeff, John, Tom St, Kanwar, Pankaj, Lee, David, Liao, Jeffery, Lokhmotov, Anton, Massa, Francisco, Meng, Peng, Micikevicius, Paulius, Osborne, Colin, Pekhimenko, Gennady, Rajan, Arun Tejusve Raghunath, Sequeira, Dilip, Sirasao, Ashish, Sun, Fei, Tang, Hanlin, Thomson, Michael, Wei, Frank, Wu, Ephrem, Xu, Lingjie, Yamada, Koichi, Yu, Bing, Yuan, George, Zhong, Aaron, Zhang, Peizhao, Zhou, Yuchen

“…Machine-learning (ML) hardware and software system demand is burgeoning. Driven by ML applications, the number of different ML inference systems has exploded…”
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Taking Your AI Projects from Pilot to Production by Diamos, Greg

“…[...]when pushed into the real world with real-world constraints like variable environmental conditions, real-time requirements, and integrations with existing…”
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Beyond Human-Level Accuracy: Computational Challenges in Deep Learning by Hestness, Joel, Ardalani, Newsha, Diamos, Greg

“…Deep learning (DL) research yields accuracy and product improvements from both model architecture changes and scale: larger data sets and models, and more…”
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Data Engineering for Everyone by Reddi, Vijay Janapa, Diamos, Greg, Warden, Pete, Mattson, Peter, Kanter, David

“…Data engineering is one of the fastest-growing fields within machine learning (ML). As ML becomes more common, the appetite for data grows more ravenous. But…”
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The People's Speech: A Large-Scale Diverse English Speech Recognition Dataset for Commercial Usage by Galvez, Daniel, Diamos, Greg, Ciro, Juan, Cerón, Juan Felipe, Achorn, Keith, Gopi, Anjali, Kanter, David, Lam, Maximilian, Mazumder, Mark, Reddi, Vijay Janapa

“…The People's Speech is a free-to-download 30,000-hour and growing supervised conversational English speech recognition dataset licensed for academic and…”
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EPNAS: Efficient Progressive Neural Architecture Search by Zhou, Yanqi, Wang, Peng, Arik, Sercan, Yu, Haonan, Zawad, Syed, Yan, Feng, Diamos, Greg

“…In this paper, we propose Efficient Progressive Neural Architecture Search (EPNAS), a neural architecture search (NAS) that efficiently handles large search…”
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Resource-Efficient Neural Architect by Zhou, Yanqi, Ebrahimi, Siavash, Arık, Sercan Ö, Yu, Haonan, Liu, Hairong, Diamos, Greg

“…Neural Architecture Search (NAS) is a laborious process. Prior work on automated NAS targets mainly on improving accuracy, but lacks consideration of…”
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DataPerf: Benchmarks for Data-Centric AI Development by Mazumder, Mark, Banbury, Colby, Yao, Xiaozhe, Karlaš, Bojan, Rojas, William Gaviria, Diamos, Sudnya, Diamos, Greg, He, Lynn, Parrish, Alicia, Kirk, Hannah Rose, Quaye, Jessica, Rastogi, Charvi, Kiela, Douwe, Jurado, David, Kanter, David, Mosquera, Rafael, Ciro, Juan, Aroyo, Lora, Acun, Bilge, Chen, Lingjiao, Raje, Mehul Smriti, Bartolo, Max, Eyuboglu, Sabri, Ghorbani, Amirata, Goodman, Emmett, Inel, Oana, Kane, Tariq, Kirkpatrick, Christine R, Kuo, Tzu-Sheng, Mueller, Jonas, Thrush, Tristan, Vanschoren, Joaquin, Warren, Margaret, Williams, Adina, Yeung, Serena, Ardalani, Newsha, Paritosh, Praveen, Bat-Leah, Lilith, Zhang, Ce, Zou, James, Wu, Carole-Jean, Coleman, Cody, Ng, Andrew, Mattson, Peter, Reddi, Vijay Janapa

“…Machine learning research has long focused on models rather than datasets, and prominent datasets are used for common ML tasks without regard to the breadth,…”
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MLPerf Training Benchmark by Mattson, Peter, Cheng, Christine, Coleman, Cody, Diamos, Greg, Micikevicius, Paulius, Patterson, David, Tang, Hanlin, Wei, Gu-Yeon, Bailis, Peter, Bittorf, Victor, Brooks, David, Chen, Dehao, Dutta, Debojyoti, Gupta, Udit, Hazelwood, Kim, Hock, Andrew, Huang, Xinyuan, Ike, Atsushi, Jia, Bill, Kang, Daniel, Kanter, David, Kumar, Naveen, Liao, Jeffery, Ma, Guokai, Narayanan, Deepak, Oguntebi, Tayo, Pekhimenko, Gennady, Pentecost, Lillian, Reddi, Vijay Janapa, Robie, Taylor, John, Tom St, Tabaru, Tsuguchika, Wu, Carole-Jean, Xu, Lingjie, Yamazaki, Masafumi, Young, Cliff, Zaharia, Matei

“…Machine learning (ML) needs industry-standard performance benchmarks to support design and competitive evaluation of the many emerging software and hardware…”
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MLPerf Inference Benchmark by Reddi, Vijay Janapa, Cheng, Christine, Kanter, David, Mattson, Peter, Schmuelling, Guenther, Wu, Carole-Jean, Anderson, Brian, Breughe, Maximilien, Charlebois, Mark, Chou, William, Chukka, Ramesh, Coleman, Cody, Davis, Sam, Deng, Pan, Diamos, Greg, Duke, Jared, Fick, Dave, Gardner, J. Scott, Hubara, Itay, Idgunji, Sachin, Jablin, Thomas B, Jiao, Jeff, John, Tom St, Kanwar, Pankaj, Lee, David, Liao, Jeffery, Lokhmotov, Anton, Massa, Francisco, Meng, Peng, Micikevicius, Paulius, Osborne, Colin, Pekhimenko, Gennady, Rajan, Arun Tejusve Raghunath, Sequeira, Dilip, Sirasao, Ashish, Sun, Fei, Tang, Hanlin, Thomson, Michael, Wei, Frank, Wu, Ephrem, Xu, Lingjie, Yamada, Koichi, Yu, Bing, Yuan, George, Zhong, Aaron, Zhang, Peizhao, Zhou, Yuchen

“…Machine-learning (ML) hardware and software system demand is burgeoning. Driven by ML applications, the number of different ML inference systems has exploded…”
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Deep Speech: Scaling up end-to-end speech recognition by Hannun, Awni, Case, Carl, Casper, Jared, Catanzaro, Bryan, Diamos, Greg, Elsen, Erich, Prenger, Ryan, Satheesh, Sanjeev, Sengupta, Shubho, Coates, Adam, Ng, Andrew Y

“…We present a state-of-the-art speech recognition system developed using end-to-end deep learning. Our architecture is significantly simpler than traditional…”
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Deep Speech 2: End-to-End Speech Recognition in English and Mandarin by Amodei, Dario, Anubhai, Rishita, Battenberg, Eric, Case, Carl, Casper, Jared, Catanzaro, Bryan, Chen, Jingdong, Chrzanowski, Mike, Coates, Adam, Diamos, Greg, Elsen, Erich, Engel, Jesse, Fan, Linxi, Fougner, Christopher, Han, Tony, Hannun, Awni, Jun, Billy, LeGresley, Patrick, Lin, Libby, Narang, Sharan, Ng, Andrew, Ozair, Sherjil, Prenger, Ryan, Raiman, Jonathan, Satheesh, Sanjeev, Seetapun, David, Sengupta, Shubho, Wang, Yi, Wang, Zhiqian, Wang, Chong, Xiao, Bo, Yogatama, Dani, Zhan, Jun, Zhu, Zhenyao

“…We show that an end-to-end deep learning approach can be used to recognize either English or Mandarin Chinese speech--two vastly different languages. Because…”
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