Feb 28, 2020 · Artificial Intelligence

TPNN Multi‑GPU Training and Mobile Optimization for Children's Acoustic Speech Recognition Models

This article describes the TPNN deep‑learning platform’s multi‑GPU acceleration, data‑parallel BMUF training, LSTM‑CTC acoustic modeling, and a suite of mobile‑side optimizations—including model pruning, 8‑bit quantization, low‑precision matrix multiplication and mixed‑precision computation—that together achieve over 92% recognition accuracy for children’s English speech on both server and mobile devices.

BMUFCTCDeep Learning

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TPNN Multi‑GPU Training and Mobile Optimization for Children's Acoustic Speech Recognition Models

Alibaba Cloud Developer

Jun 5, 2017 · Artificial Intelligence

Alibaba’s Distributed Training Boosts Neural Machine Translation Speed

Since its 2013 debut, Neural Machine Translation (NMT) has approached human quality, but training costs are high; Alibaba’s team developed a distributed NMT system in 2017, employing data‑parallel, model‑average, BMUF, Downpour SGD, and Ring‑allReduce techniques to cut training time from over 20 days to a few days while maintaining translation quality.

BMUFDistributed TrainingDownpour SGD

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Alibaba’s Distributed Training Boosts Neural Machine Translation Speed