@inproceedings{c230f23c63f544019fc52d4f5a023d52,
title = "Interleaved Data Processing Scheme for Optimizing Tensorflow Framework",
abstract = "Nowadays, technologies based on machine learning (ML) are becoming a popular solution for building recommendation services on consumer electronics (CE) devices. Therefore, in this paper, we introduce i.data (interleaved data) processing scheme to more efficiently enable ML models on CE devices. The key idea of i.data is to interleave the training and inference steps in a parallel way so as to improve the GPU utilization and overall performance. We also compared i.data with Numpy and tf.data that are widely used to build ML models in various environments including CE and the internet of things (IoT). Our experimental results clearly show that i.data improves the performance compared with the traditional data processing approaches.",
keywords = "dataflow, machine learning, multiple thread, tensorflow, tf.data",
author = "Jinseo Choi and Minseon Cho and Donghyun Kang",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 11th IEEE International Conference on Consumer Electronics, ICCE-Berlin 2021 ; Conference date: 15-11-2021 Through 18-11-2021",
year = "2021",
doi = "10.1109/ICCE-Berlin53567.2021.9720006",
language = "English",
series = "IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin",
publisher = "IEEE Computer Society",
booktitle = "2021 IEEE 11th International Conference on Consumer Electronics, ICCE-Berlin 2021",
address = "United States",
}