# Development of JavaScript-based deep learning platform and application to distributed training

## TL;DR

This paper presents a JavaScript-based deep learning framework that enables training large neural networks in distributed environments directly within web browsers, reducing deployment costs and increasing accessibility.

## Contribution

The authors developed a JavaScript framework for deep learning that operates on standard browsers and smartphones, facilitating distributed training without specialized hardware or software installation.

## Key findings

- Successfully trained VGGNet in a distributed manner using web browsers.
- Demonstrated practical training of large-scale CNNs like ResNet on ordinary devices.
- Framework leverages WebCL GPGPU for efficient computation.

## Abstract

Deep learning is increasingly attracting attention for processing big data. Existing frameworks for deep learning must be set up to specialized computer systems. Gaining sufficient computing resources therefore entails high costs of deployment and maintenance. In this work, we implement a matrix library and deep learning framework that uses JavaScript. It can run on web browsers operating on ordinary personal computers and smartphones. Using JavaScript, deep learning can be accomplished in widely diverse environments without the necessity for software installation. Using GPGPU from WebCL framework, our framework can train large scale convolutional neural networks such as VGGNet and ResNet. In the experiments, we demonstrate their practicality by training VGGNet in a distributed manner using web browsers as the client.