Paper Info
Title
TensorFlow: A system for large-scale machine learning.
Abstract
TensorFlow is a machine learning system that operates at large scale and in heterogeneous environments. Tensor-Flow uses dataflow graphs to represent computation, shared state, and the operations that mutate that state. It maps the nodes of a dataflow graph across many machines in a cluster, and within a machine across multiple computational devices, including multicore CPUs, general-purpose GPUs, and custom-designed ASICs known as Tensor Processing Units (TPUs). This architecture gives flexibility to the application developer: whereas in previous \"parameter server\" designs the management of shared state is built into the system, TensorFlow enables developers to experiment with novel optimizations and training algorithms. TensorFlow supports a variety of applications, with a focus on training and inference on deep neural networks. Several Google services use TensorFlow in production, we have released it as an open-source project, and it has become widely used for machine learning research. In this paper, we describe the TensorFlow dataflow model and demonstrate the compelling performance that TensorFlow achieves for several real-world applications.
Year
Venue
DocType
2016
OSDI
Conference
Volume
Citations 
PageRank 
abs/1605.08695
701
16.23
References 
Authors
52
22
Search Limit
100701
Name
Order
Citations
PageRank
Martín Abadi1120741324.31
Paul Barham26162459.58
Jianmin Chen389528.70
Zhifeng Chen42747106.75
A. Davis5159353.13
Jeffrey Dean611804457.69
Matthieu Devin7146547.70
Sanjay Ghemawat8132421185.52
Geoffrey Irving9210178.49
Michael Isard109533729.89
Manjunath Kudlur11199771.21
Josh Levenberg12144446.60
Rajat Monga13165460.93
Sherry Moore14144445.92
Derek Gordon Murray15209083.65
Benoit Steiner16148649.38
Paul A. Tucker17144445.92
Vijay Vasudevan18174164.21
Pete Warden19146247.58
Martin Wicke20214073.79
Yuan Yu212955149.84
Xiaoqiang Zhang2270116.23