Deep learning is a rapidly growing field of machine learning, and has proven successful in many domains, including computer vision, language translation, and speech recognition. The training of deep neural networks is resource intensive, requiring compute accelerators such as GPUs, as well as large amounts of storage and memory, and network bandwidth. Additionally, getting the training data ready requires a lot of tooling for data cleansing, data merging, ambiguity resolution, etc. Sophisticated middleware abstractions are needed to schedule resources, manage the distributed training job as well as visualize how well the training is progressing. Likewise, serving the large neural network models with low latency constraints can require middleware to manage model caching, selection, and refinement.

All the major cloud providers, including Amazon, Google, IBM, and Microsoft have started to offer cloud services in the last year or so with services to train and/or serve deep neural network models. In addition, there is a lot of activity in open source middleware for deep learning, including Tensorflow, Theano, Caffe2, PyTorch, and MXNet. There are also efforts to extend existing platforms such as Spark for deep learning workloads.

This workshop focuses on the tools, frameworks, and algorithms to support executing deep learning algorithms in a distributed environment. As new hardware and accelerators become available, the middleware and systems need to be able exploit their capabilities and ensure they are utilized efficiently.

Authors are invited to submit research papers, experience papers, demonstrations, or position papers

Topics
This workshop solicits papers from both academia and industry on the state of practice and state of the art in deep learning infrastructures. Topics of interest include but are not limited to:
Resource scheduling algorithms for deep learning workloads
Advances in deep learning frameworks
Programming abstractions for deep learning models
Middleware support for hardware accelerators
Novel distribution techniques for training large neural networks
Case studies of deep learning middleware
Optimization techniques for Inferencing
Novel debugging and logging techniques
Data cleansing, data disambiguation tools for deep learning
Data visualization tools for deep learning
Dates and location
Paper submissions: September 29, 2017
Notification to authors: October 21, 2017
Camera-ready copy due: October 28, 2017

The DIDL workshop is co-located with the Middleware conference, which will be held in Las Vegas, USA from December 11-15, 2017.

Papers and Submissions
We are looking for the following types of submissions:
Research and industry papers (up to 6 pages): Reports on original results including novel techniques, significant case studies or surveys. Authors may include extra material beyond the six pages as a clearly marked appendix, which reviewers are not obliged to read but could read.
Position papers (up to 4 pages): Reports identifying unaddressed problems and research challenges.
Abstracts (up to 1 page): An extended abstract on a preliminary or ongoing work.
Papers must be written in English and submitted in PDF format. All papers should follow ACM formatting instructions, specifically the ACM SIG Proceedings Standard Style. The author kit containing the templates for the required style can be found at http://www.acm.org/publications/proceedings-template.

Submissions should not be blinded for review. Please submit your papers via the submission site: https://didl17.hotcrp.com/

All accepted papers will appear in the Middleware 2017 companion proceedings, available in the ACM Digital Library. All accepted papers will also be presented at the workshop, and at least one author of each paper must register for the workshop.

Workshop Co-chairs
Bishwaranjan Bhattacharjee, IBM Research
Vatche Ishakian, Bentley University
Hans-Arno Jacobsen, MIddleware Systems Research Group
Vinod Muthusamy, IBM Research


Program Committee
Ian Foster, Argonne National Laboratory and the University of Chicago
Benoit Huet, Eurecom
Pietro Michiardi, Eurecom
Peter Pietzuch, Imperial College
Evgenia Smirni, College of William and Mary
Yandong Wang, Citadel Securities
Chuan Wu, University of Hong Kong