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pro vyhledávání: '"Fegade, Pratik"'
Dynamic control flow is an important technique often used to design expressive and efficient deep learning computations for applications such as text parsing, machine translation, exiting early out of deep models and so on. The control flow divergenc
Externí odkaz:
http://arxiv.org/abs/2305.10611
Batching has a fundamental influence on the efficiency of deep neural network (DNN) execution. However, for dynamic DNNs, efficient batching is particularly challenging as the dataflow graph varies per input instance. As a result, state-of-the-art fr
Externí odkaz:
http://arxiv.org/abs/2302.03851
There is often variation in the shape and size of input data used for deep learning. In many cases, such data can be represented using tensors with non-uniform shapes, or ragged tensors. Due to limited and non-portable support for efficient execution
Externí odkaz:
http://arxiv.org/abs/2110.10221
Optimizing deep learning models is generally performed in two steps: (i) high-level graph optimizations such as kernel fusion and (ii) low level kernel optimizations such as those found in vendor libraries. This approach often leaves significant perf
Externí odkaz:
http://arxiv.org/abs/2011.01383
Autor:
Fegade, Pratik
Deep learning has increasingly begun to be used across a wide range of computing applications. Dynamism—the property where the execution of a computation differs in some way across different inputs— has been shown to be an important tool in enabl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0f840cae42ec3149acc7c67b242a1b95