Parallel and Distributed Deep Learning Paper Database
The paper database below was collected for the purpose of the paper "Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis". It contains works that utilize parallel and distributed computing resources for training Deep Neural Networks. This includes hardware architectures, data representation, parallelization strategies, distributed algorithms, system implementations, frameworks, and programming models.
The papers are organized in the YAML format, with one entry per paper, sorted by publication year. Each entry contains the paper title, year of publication, category (corresponding to sections in the paper), keywords, datasets used in experiments, frameworks, and hardware architectures.
The following listing shows an example of such a paper entry:
|papers.yml - (Coming Soon)||February 26, 2018||First Release|
| T. Ben-Nun, T. Hoefler:|
|Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis
CoRR. Vol abs/1802.09941, Feb. 2018, |