Search Results for author: Tristan J. Webb

Found 2 papers, 1 papers with code

Intel nGraph: An Intermediate Representation, Compiler, and Executor for Deep Learning

1 code implementation • 24 Jan 2018 • Scott Cyphers, Arjun K. Bansal, Anahita Bhiwandiwalla, Jayaram Bobba, Matthew Brookhart, Avijit Chakraborty, Will Constable, Christian Convey, Leona Cook, Omar Kanawi, Robert Kimball, Jason Knight, Nikolay Korovaiko, Varun Kumar, Yixing Lao, Christopher R. Lishka, Jaikrishnan Menon, Jennifer Myers, Sandeep Aswath Narayana, Adam Procter, Tristan J. Webb

The current approach, which we call "direct optimization", requires deep changes within each framework to improve the training performance for each hardware backend (CPUs, GPUs, FPGAs, ASICs) and requires $\mathcal{O}(fp)$ effort; where $f$ is the number of frameworks and $p$ is the number of platforms.

graph partitioning Management +1

215

Paper
Code

Flexpoint: An Adaptive Numerical Format for Efficient Training of Deep Neural Networks

no code implementations • NeurIPS 2017 • Urs Köster, Tristan J. Webb, Xin Wang, Marcel Nassar, Arjun K. Bansal, William H. Constable, Oğuz H. Elibol, Scott Gray, Stewart Hall, Luke Hornof, Amir Khosrowshahi, Carey Kloss, Ruby J. Pai, Naveen Rao

Here we present the Flexpoint data format, aiming at a complete replacement of 32-bit floating point format training and inference, designed to support modern deep network topologies without modifications.

Generative Adversarial Network

Paper
Add Code

Cannot find the paper you are looking for? You can Submit a new open access paper.