Wildfire: Approximate synchronization of parameters in distributed deep learning

By R. Nair, S. Gupta. 2017

9 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Chia-Yu Chen
2. Jungwook Choi
3. Daniel Brand
4. Ankur Agrawal
5. Kailash Gopalakrishnan
6. Tijmen Blankevoort
7. Markus Nagel
8. Deniz Gündüz

In Electrical and Electronic Engineering

1. Kailash Gopalakrishnan

Citations in the corpus

• AdaComp : Adaptive Residual Gradient Compression for Data-Parallel Distributed Training (2017, methodology): “…system topologies have also been explored to efficiently implement distributed training using data parallelism techniques ranging from simple parameter-server based approaches to the recently proposed Wild-Fire technique (Nair and Gupta 2017) that allows direct exchange of weights during reduction.”