Add to ORKGHigh-performance tensor programs are crucial to guarantee efficient execution of deep neural networks. However, obtaining performant tensor programs for different operators on various hardware platforms is notoriously challenging. Currently, deep learning systems rely on vendor-provided kernel libraries or various search strategies to get performant tensor programs. These approaches either require significant engineering effort to develop platform-specific optimization code or fall short of finding high-performance programs due to restricted search space and ineffective exploration strategy. We present Ansor, a tensor program generation framework for deep learning applications. Compared with existing search strategies, Ansor explores many...
Memory efficiency is crucial in training deep learning networks on resource-restricted devices. Duri...
Introduction In the last two decade, tensor computations developed from a small and little known su...
We introduce deep tensor networks, which are exponentially wide neural networks based on the tensor ...
Boosting the runtime performance of deep neural networks (DNNs) is critical due to their wide adopti...
Deploying deep learning models on various devices has become an important topic. The wave of hardwar...
Auto-scheduling for tensor programs is a process where a search algorithm automatically explores can...
Automatic optimization for tensor programs becomes increasingly important as we deploy deep learning...
140 pagesTensor algebra lives at the heart of big data applications. Where classical machine learnin...
International audienceA wide range of scientific and machine learning applications depend on highly ...
The emergence of deep learning has launched many works in deep learning accelerators. To fully reali...
Popular Machine Learning (ML) and High Performance Computing (HPC) workloads contribute to a signifi...
Training deep neural networks consumes increasing computational resource shares in many compute cent...
Training and inference efficiency of deep neural networks highly rely on the performance of tensor o...
Learning neural fields has been an active topic in deep learning research, focusing, among other iss...
To efficiently perform inference with neural networks, the underlying tensor programs require suffic...
Memory efficiency is crucial in training deep learning networks on resource-restricted devices. Duri...
Introduction In the last two decade, tensor computations developed from a small and little known su...
We introduce deep tensor networks, which are exponentially wide neural networks based on the tensor ...
Boosting the runtime performance of deep neural networks (DNNs) is critical due to their wide adopti...
Deploying deep learning models on various devices has become an important topic. The wave of hardwar...
Auto-scheduling for tensor programs is a process where a search algorithm automatically explores can...
Automatic optimization for tensor programs becomes increasingly important as we deploy deep learning...
140 pagesTensor algebra lives at the heart of big data applications. Where classical machine learnin...
International audienceA wide range of scientific and machine learning applications depend on highly ...
The emergence of deep learning has launched many works in deep learning accelerators. To fully reali...
Popular Machine Learning (ML) and High Performance Computing (HPC) workloads contribute to a signifi...
Training deep neural networks consumes increasing computational resource shares in many compute cent...
Training and inference efficiency of deep neural networks highly rely on the performance of tensor o...
Learning neural fields has been an active topic in deep learning research, focusing, among other iss...
To efficiently perform inference with neural networks, the underlying tensor programs require suffic...
Memory efficiency is crucial in training deep learning networks on resource-restricted devices. Duri...
Introduction In the last two decade, tensor computations developed from a small and little known su...
We introduce deep tensor networks, which are exponentially wide neural networks based on the tensor ...