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About this paper
Daniel Povey, Xiaohui Zhang, Sanjeev Khudanpur
This paper from Johns Hopkins University's CLSP and HLTCOE proposes a method for parallel training of deep neural networks by combining periodic parameter averaging with an efficient approximate Natural Gradient Stochastic Gradient Descent. This approach enables near-linear speedup in training large speech recognition models on multiple GPUs while significantly reducing network communication overhead, leading to improved convergence and lower Word Error Rates.
Key insights
- Periodic parameter averaging alone performs poorly; its effectiveness is critically dependent on the use of Natural Gradient SGD, which stabilizes individual training processes.
- Approximate Natural Gradient improves the intrinsic convergence properties of SGD, making it more robust to the infrequent synchronization of parallel training.
- The combination allows for efficient, communication-light scaling of DNN training across multiple machines or GPUs, reducing reliance on high-bandwidth networks.
Summary by alphaXiv.
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