Search Results for author: Tae Jun Ham

Found 2 papers, 1 papers with code

L3: Accelerator-Friendly Lossless Image Format for High-Resolution, High-Throughput DNN Training

1 code implementation18 Aug 2022 Jonghyun Bae, Woohyeon Baek, Tae Jun Ham, Jae W. Lee

The decoding process of L3 is effectively parallelized on the accelerator, thus minimizing CPU intervention for data preparation during DNN training.

Vocal Bursts Intensity Prediction

A$^3$: Accelerating Attention Mechanisms in Neural Networks with Approximation

no code implementations22 Feb 2020 Tae Jun Ham, Sung Jun Jung, Seonghak Kim, Young H. Oh, Yeonhong Park, Yoonho Song, Jung-Hun Park, Sanghee Lee, Kyoung Park, Jae W. Lee, Deog-Kyoon Jeong

The attention mechanism is widely adopted by many state-of-the-art neural networks for computer vision, natural language processing, and machine translation, and accounts for a large portion of total execution time.

Machine Translation Translation

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