Zobrazeno 1 - 10
of 34
pro vyhledávání: '"Roesch, Jared"'
Autor:
Lai, Ruihang, Shao, Junru, Feng, Siyuan, Lyubomirsky, Steven S., Hou, Bohan, Lin, Wuwei, Ye, Zihao, Jin, Hongyi, Jin, Yuchen, Liu, Jiawei, Jin, Lesheng, Cai, Yaxing, Jiang, Ziheng, Wu, Yong, Park, Sunghyun, Srivastava, Prakalp, Roesch, Jared G., Mowry, Todd C., Chen, Tianqi
Dynamic shape computations have become critical in modern machine learning workloads, especially in emerging large language models. The success of these models has driven demand for deploying them to a diverse set of backend environments. In this pap
Externí odkaz:
http://arxiv.org/abs/2311.02103
Autor:
Chen, Zhi, Yu, Cody Hao, Morris, Trevor, Tuyls, Jorn, Lai, Yi-Hsiang, Roesch, Jared, Delaye, Elliott, Sharma, Vin, Wang, Yida
Deep neural networks (DNNs) have been ubiquitously applied in many applications, and accelerators are emerged as an enabler to support the fast and efficient inference tasks of these applications. However, to achieve high model coverage with high per
Externí odkaz:
http://arxiv.org/abs/2105.03215
Autor:
Kirisame, Marisa, Lyubomirsky, Steven, Haan, Altan, Brennan, Jennifer, He, Mike, Roesch, Jared, Chen, Tianqi, Tatlock, Zachary
Checkpointing enables the training of deep learning models under restricted memory budgets by freeing intermediate activations from memory and recomputing them on demand. Current checkpointing techniques statically plan these recomputations offline a
Externí odkaz:
http://arxiv.org/abs/2006.09616
Autor:
Shen, Haichen, Roesch, Jared, Chen, Zhi, Chen, Wei, Wu, Yong, Li, Mu, Sharma, Vin, Tatlock, Zachary, Wang, Yida
Publikováno v:
In Proceedings of the 4th Conference on Machine Learning and Systems (MLSys 2021)
Modern deep neural networks increasingly make use of features such as dynamic control flow, data structures and dynamic tensor shapes. Existing deep learning systems focus on optimizing and executing static neural networks which assume a pre-determin
Externí odkaz:
http://arxiv.org/abs/2006.03031
Autor:
Roesch, Jared, Lyubomirsky, Steven, Kirisame, Marisa, Weber, Logan, Pollock, Josh, Vega, Luis, Jiang, Ziheng, Chen, Tianqi, Moreau, Thierry, Tatlock, Zachary
Frameworks for writing, compiling, and optimizing deep learning (DL) models have recently enabled progress in areas like computer vision and natural language processing. Extending these frameworks to accommodate the rapidly diversifying landscape of
Externí odkaz:
http://arxiv.org/abs/1904.08368
Autor:
Jun, Eunice, Daum, Maureen, Roesch, Jared, Chasins, Sarah E., Berger, Emery D., Just, Rene, Reinecke, Katharina
Though statistical analyses are centered on research questions and hypotheses, current statistical analysis tools are not. Users must first translate their hypotheses into specific statistical tests and then perform API calls with functions and param
Externí odkaz:
http://arxiv.org/abs/1904.05387
Autor:
Roesch, Jared, Lyubomirsky, Steven, Weber, Logan, Pollock, Josh, Kirisame, Marisa, Chen, Tianqi, Tatlock, Zachary
Machine learning powers diverse services in industry including search, translation, recommendation systems, and security. The scale and importance of these models require that they be efficient, expressive, and portable across an array of heterogeneo
Externí odkaz:
http://arxiv.org/abs/1810.00952
Autor:
Moreau, Thierry, Chen, Tianqi, Vega, Luis, Roesch, Jared, Yan, Eddie, Zheng, Lianmin, Fromm, Josh, Jiang, Ziheng, Ceze, Luis, Guestrin, Carlos, Krishnamurthy, Arvind
Specialized Deep Learning (DL) acceleration stacks, designed for a specific set of frameworks, model architectures, operators, and data types, offer the allure of high performance while sacrificing flexibility. Changes in algorithms, models, operator
Externí odkaz:
http://arxiv.org/abs/1807.04188
Deciding the equivalence of SQL queries is a fundamental problem in data management. As prior work has mainly focused on studying the theoretical limitations of the problem, very few implementations for checking such equivalences exist. In this paper
Externí odkaz:
http://arxiv.org/abs/1802.02229
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