Zobrazeno 1 - 10
of 22
pro vyhledávání: '"Anton Lokhmotov"'
Autor:
Grigori Fursin, Anton Lokhmotov, Bruno Carpentieri, Fabiana Zollo, Marco Cianfriglia, Damiano Perri, Osvaldo Gervasi, Paolo Sylos Labini, Cedric Nugteren, Flavio Vella
Publikováno v:
ACM Transactions on Architecture and Code Optimization. 18:1-24
Efficient HPC libraries often expose multiple tunable parameters, algorithmic implementations, or a combination of them, to provide optimized routines. The optimal parameters and algorithmic choices may depend on input properties such as the shapes o
Autor:
Alexey Natykan, Elena L. Vodovozova, Natalia R. Onishchenko, Diana Arantseva, Anton Lokhmotov, Ivan A. Boldyrev, Dmitry Shobolov, Elena V. Svirshchevskaya, Daria Tretiakova, Anna Alekseeva, Roman Kamyshinsky
Publikováno v:
Current Drug Delivery. 17:312-323
Background:: Recently we developed a scalable scheme of synthesis of melphalan ester conjugate with 1,2-dioleoyl-sn-glycerol (MlphDG) and a protocol for the fabrication of its lyophilized liposomal formulation. Objective: Herein we compared this new
Autor:
Mark Charlebois, Dave Fick, Sachin Satish Idgunji, Yuchen Zhou, Michael Thomson, Ashish Sirasao, George Yuan, Anton Lokhmotov, Koichi Yamada, Tom St. John, Bing Yu, Jeff Jiao, Arun Tejusve Raghunath Rajan, Paulius Micikevicius, Ephrem C. Wu, Francisco Massa, Carole-Jean Wu, Hanlin Tang, David Lee, William Chou, Frank Wei, Jared Duke, Cody Coleman, Sam Davis, Jeffery Liao, Itay Hubara, Dilip Sequeira, Lingjie Xu, Pan Deng, Vijay Janapa Reddi, Guenther Schmuelling, Gennady Pekhimenko, Maximilien Breughe, Peng Meng, Greg Diamos, David Kanter, Colin Osborne, Thomas B. Jablin, Peizhao Zhang, Fei Sun, Pankaj Kanwar, Ramesh Chukka, J. Scott Gardner, Aaron Zhong, Christine Cheng, Peter Mattson, Brian M. Anderson
Publikováno v:
ISCA
Machine-learning (ML) hardware and software system demand is burgeoning. Driven by ML applications, the number of different ML inference systems has exploded. Over 100 organizations are building ML inference chips, and the systems that incorporate ex
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7a2417894de20d08d29ba667a4080f9a
Autor:
Johan Gronqvist, Anton Lokhmotov
Publikováno v:
GPU Pro 360 Guide to Mobile Devices ISBN: 9781351138000
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fb10919bd6aa1313ce1e258ae5d531bb
https://doi.org/10.1201/9781351138000-18
https://doi.org/10.1201/9781351138000-18
Publikováno v:
ReQuEST@ASPLOS
We present a customizable Collective Knowledge workflow to study the execution time vs. accuracy trade-offs for the MobileNets CNN family. We use this workflow to evaluate MobileNets on Arm Cortex CPUs using TensorFlow and Arm Mali GPUs using several
Autor:
Ganesh Gopalakrishnan, Shaz Qadeer, Guodong Li, Peng Li, Nathan Chong, Jeroen Ketema, Anton Lokhmotov, Alastair F. Donaldson
Graphics processing units (GPU)-accelerated computing is being adopted increasingly in a number of areas, ranging from high-end scientific computing to mobile and embedded computing. While GPU programs routinely provide high computational throughput
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e58036a9b2e292a02502236624718c0f
https://doi.org/10.1016/b978-0-12-803738-6.00001-x
https://doi.org/10.1016/b978-0-12-803738-6.00001-x
Autor:
Davide Del Vento, Marc Baboulin, Michael Gerndt, Diego Novillo, Allen D. Malony, Renato Miceli, Zbigniew Chamski, Anton Lokhmotov, Grigori Fursin
Publikováno v:
Scientific Programming
Scientific Programming, IOS Press, 2014, Automatic Application Tuning for HPC Architectures, 22 (4), pp.309-329. ⟨10.3233/SPR-140396⟩
Scientific Programming, Vol 22, Iss 4, Pp 309-329 (2014)
Scientific Programming, 2014, Automatic Application Tuning for HPC Architectures, 22 (4), pp.309-329. ⟨10.3233/SPR-140396⟩
Scientific Programming, IOS Press, 2014, Automatic Application Tuning for HPC Architectures, 22 (4), pp.309-329. ⟨10.3233/SPR-140396⟩
Scientific Programming, Vol 22, Iss 4, Pp 309-329 (2014)
Scientific Programming, 2014, Automatic Application Tuning for HPC Architectures, 22 (4), pp.309-329. ⟨10.3233/SPR-140396⟩
Empirical auto-tuning and machine learning techniques have been showing high potential to improve execution time, power consumption, code size, reliability and other important metrics of various applications for more than two decades. However, they a
Autor:
Grigori Fursin, Anton Lokhmotov
Publikováno v:
IWOCL
Autotuning is a popular technique to ensure performance portability for important algorithms such as BLAS, FFT and DNN across the ever evolving software and hardware stack. Unfortunately, when performed on a single machine, autotuning can explore onl
Autor:
Grigori Fursin, Anton Lokhmotov
Publikováno v:
IWOCL
We invite the community to collaboratively design and optimize convolutional neural networks to meet the performance, accuracy and cost requirements for deployment on a range of form factors -- from sensors to self-driving cars.
Publikováno v:
DATE
Research funding bodies strongly encourage research projects to disseminate discovered knowledge and transfer developed technology to industry. Unfortunately, capturing, sharing, reproducing and building upon experimental results has become close to