Implementation of a Practical Distributed Calculation System with Browsers and JavaScript, and Application to Distributed Deep Learning
This work addresses the problem of making deep learning more accessible and scalable for users with limited hardware by leveraging web browsers, though it is incremental in applying existing distributed concepts to a new platform.
The authors tackled the high computational demands of deep learning by developing a distributed calculation framework (Sashimi) and a JavaScript neural network framework (Sukiyaki) that enable distributed deep learning using web browsers, achieving a 30x speedup in deep CNN learning compared to conventional JavaScript libraries.
Deep learning can achieve outstanding results in various fields. However, it requires so significant computational power that graphics processing units (GPUs) and/or numerous computers are often required for the practical application. We have developed a new distributed calculation framework called "Sashimi" that allows any computer to be used as a distribution node only by accessing a website. We have also developed a new JavaScript neural network framework called "Sukiyaki" that uses general purpose GPUs with web browsers. Sukiyaki performs 30 times faster than a conventional JavaScript library for deep convolutional neural networks (deep CNNs) learning. The combination of Sashimi and Sukiyaki, as well as new distribution algorithms, demonstrates the distributed deep learning of deep CNNs only with web browsers on various devices. The libraries that comprise the proposed methods are available under MIT license at http://mil-tokyo.github.io/.