Add to ORKGMany state-of-the-art deep learning models rely on dynamic computation logic, making them difficult to optimize. In this thesis, we present a hashing based algorithm that is able to detect and optimize computation logic common to different computation graphs. We show that our algorithm can be integrated seamlessly into popular deep learning frameworks such as TensorFlow, with nearly zero code changes required on the part of users in order to adapt our optimizations to their programs. Experiments show that our algorithm achieves 1.35× speedup on a sentiment classification task trained with the popular Tree-LSTM model
Machine learning workflow development is a process of trial-and-error: developers iterate on workflo...
Deep Learning has become one of the most important tools in computer science in the last decade beca...
Machine learning (ML) is now commonplace, powering data-driven applications in various organizations...
Many state-of-the-art deep learning models rely on dynamic computation logic, making them difficult t...
Rapid progress in deep learning is leading to a diverse set of quickly changing models, with a drama...
In recent years, machine learning (ML) and, more noticeably, deep learning (DL), have be- come incre...
Thesis (Ph.D.)--University of Washington, 2022As the scaling and performance demands for deep learni...
Machine Learning (ML) frameworks are tools that facilitate the development and deployment of ML mode...
The emergence of deep learning has launched many works in deep learning accelerators. To fully reali...
Computers are powerful tools which perform fast, accurate calculations over huge sets of data. Howev...
peer reviewedWith renewed global interest for Artificial Intelligence (AI) methods, the past decade ...
Computational intensive applications such as pattern recognition, and natural language processing, a...
International audienceIt is common to evaluate the performance of a machine learning model by measur...
A large portion of data mining and analytic services use modern machine learning techniques, such as...
Deep learning frameworks optimize the computation graphs and intra-operator computations to boost th...
Machine learning workflow development is a process of trial-and-error: developers iterate on workflo...
Deep Learning has become one of the most important tools in computer science in the last decade beca...
Machine learning (ML) is now commonplace, powering data-driven applications in various organizations...
Many state-of-the-art deep learning models rely on dynamic computation logic, making them difficult t...
Rapid progress in deep learning is leading to a diverse set of quickly changing models, with a drama...
In recent years, machine learning (ML) and, more noticeably, deep learning (DL), have be- come incre...
Thesis (Ph.D.)--University of Washington, 2022As the scaling and performance demands for deep learni...
Machine Learning (ML) frameworks are tools that facilitate the development and deployment of ML mode...
The emergence of deep learning has launched many works in deep learning accelerators. To fully reali...
Computers are powerful tools which perform fast, accurate calculations over huge sets of data. Howev...
peer reviewedWith renewed global interest for Artificial Intelligence (AI) methods, the past decade ...
Computational intensive applications such as pattern recognition, and natural language processing, a...
International audienceIt is common to evaluate the performance of a machine learning model by measur...
A large portion of data mining and analytic services use modern machine learning techniques, such as...
Deep learning frameworks optimize the computation graphs and intra-operator computations to boost th...
Machine learning workflow development is a process of trial-and-error: developers iterate on workflo...
Deep Learning has become one of the most important tools in computer science in the last decade beca...
Machine learning (ML) is now commonplace, powering data-driven applications in various organizations...