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GTC ON-DEMAND

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Abstract:
We'll present our study on GPU optimization for deep learning with limited computational resources and share our tips and tricks for building a state-of-the-art Visual Question Answering (VQA) system. Learn about technical implementations of deep learning algorithms with GPU hardware utilization, including delayed updates and mixed-precision training, to deal with limited hardware resources while reduce training time and memory usage. We'll describe our experience designing a winning architecture for the VQA Challenge 2018 by applying deep learning tactics such as multi-level multi-modal fusion, parameter-interaction learning, and end-to-end optimization. Our techniques are all heavy computing tasks, so GPU programming plays an important role in advancing our work. We'll also provide convincing empirical proofs and a practical demonstration of a VQA application.
We'll present our study on GPU optimization for deep learning with limited computational resources and share our tips and tricks for building a state-of-the-art Visual Question Answering (VQA) system. Learn about technical implementations of deep learning algorithms with GPU hardware utilization, including delayed updates and mixed-precision training, to deal with limited hardware resources while reduce training time and memory usage. We'll describe our experience designing a winning architecture for the VQA Challenge 2018 by applying deep learning tactics such as multi-level multi-modal fusion, parameter-interaction learning, and end-to-end optimization. Our techniques are all heavy computing tasks, so GPU programming plays an important role in advancing our work. We'll also provide convincing empirical proofs and a practical demonstration of a VQA application.  Back
 
Topics:
AI & Deep Learning Research, AI Application, Deployment & Inference
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9824
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