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    GTC Silicon Valley-2019 ID:S9824:Surpassing State-of-the-Art VQA with Deep Learning Optimization Techniques and Limited GPU Resources

    Erman Tjiputra(AIOZ Pte Ltd),Quang Tran(AIOZ Pte Ltd)
    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.

    View the slides (pdf)