In plain words: A deep learning toolkit written in JavaScript runs inside ordinary web browsers on regular computers and phones, needing no software setup where usual frameworks require special machines. It trains large image-recognition networks using graphics chips, and trained one across many browsers at once.
Abstract · Development of JavaScript-based deep learning platform and application to distributed training
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.
Masatoshi Hidaka, Ken Miura, Tatsuya Harada
arXiv:1702.01846 · cs.CV, cs.DC · submitted Feb 7, 2017 · updated Mar 27, 2017
abstract · pdf · html · Workshop paper for ICLR2017