Zobrazeno 1 - 10
of 1 248
pro vyhledávání: '"Roy, Sudip"'
Publikováno v:
Proc. ACM Program. Lang., Vol. 6, No. OOPSLA2, Article 166. Publication date: October 2022
In the past few years, neural architecture search (NAS) has become an increasingly important tool within the deep learning community. Despite the many recent successes of NAS, however, most existing approaches operate within highly structured design
Externí odkaz:
http://arxiv.org/abs/2205.03960
Autor:
Barham, Paul, Chowdhery, Aakanksha, Dean, Jeff, Ghemawat, Sanjay, Hand, Steven, Hurt, Dan, Isard, Michael, Lim, Hyeontaek, Pang, Ruoming, Roy, Sudip, Saeta, Brennan, Schuh, Parker, Sepassi, Ryan, Shafey, Laurent El, Thekkath, Chandramohan A., Wu, Yonghui
We present the design of a new large scale orchestration layer for accelerators. Our system, Pathways, is explicitly designed to enable exploration of new systems and ML research ideas, while retaining state of the art performance for current models.
Externí odkaz:
http://arxiv.org/abs/2203.12533
Autor:
Xie, Xinfeng, Prabhu, Prakash, Beaugnon, Ulysse, Phothilimthana, Phitchaya Mangpo, Roy, Sudip, Mirhoseini, Azalia, Brevdo, Eugene, Laudon, James, Zhou, Yanqi
Multi-Chip-Modules (MCMs) reduce the design and fabrication cost of machine learning (ML) accelerators while delivering performance and energy efficiency on par with a monolithic large chip. However, ML compilers targeting MCMs need to solve complex
Externí odkaz:
http://arxiv.org/abs/2112.04041
Autor:
Yang, Yichen, Phothilimtha, Phitchaya Mangpo, Wang, Yisu Remy, Willsey, Max, Roy, Sudip, Pienaar, Jacques
One of the major optimizations employed in deep learning frameworks is graph rewriting. Production frameworks rely on heuristics to decide if rewrite rules should be applied and in which order. Prior research has shown that one can discover more opti
Externí odkaz:
http://arxiv.org/abs/2101.01332
Autor:
Zhou, Yanqi, Roy, Sudip, Abdolrashidi, Amirali, Wong, Daniel, Ma, Peter, Xu, Qiumin, Liu, Hanxiao, Phothilimthana, Phitchaya Mangpo, Wang, Shen, Goldie, Anna, Mirhoseini, Azalia, Laudon, James
Publikováno v:
NeurIPS 2020
Most compilers for machine learning (ML) frameworks need to solve many correlated optimization problems to generate efficient machine code. Current ML compilers rely on heuristics based algorithms to solve these optimization problems one at a time. H
Externí odkaz:
http://arxiv.org/abs/2010.12438
Autor:
Kaufman, Samuel J., Phothilimthana, Phitchaya Mangpo, Zhou, Yanqi, Mendis, Charith, Roy, Sudip, Sabne, Amit, Burrows, Mike
Accurate hardware performance models are critical to efficient code generation. They can be used by compilers to make heuristic decisions, by superoptimizers as a minimization objective, or by autotuners to find an optimal configuration for a specifi
Externí odkaz:
http://arxiv.org/abs/2008.01040
Autor:
Zhou, Yanqi, Roy, Sudip, Abdolrashidi, Amirali, Wong, Daniel, Ma, Peter C., Xu, Qiumin, Zhong, Ming, Liu, Hanxiao, Goldie, Anna, Mirhoseini, Azalia, Laudon, James
Runtime and scalability of large neural networks can be significantly affected by the placement of operations in their dataflow graphs on suitable devices. With increasingly complex neural network architectures and heterogeneous device characteristic
Externí odkaz:
http://arxiv.org/abs/1910.01578
Akademický článek
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In this paper, we propose a closed form approximation to the mean and variance of a new generalization of negative binomial (NGNB) distribution arising from the Extended COM-Poisson (ECOMP) distribution developed by Chakraborty and Imoto (2016)(see [
Externí odkaz:
http://arxiv.org/abs/1904.12459
Publikováno v:
In Journal of Molecular Liquids 15 February 2022 348