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DIDL 2020 : Fourth Workshop on Distributed Infrastructures for Deep Learning

UC Davis, CA
Event Date: Dec 09, 2020 - Dec 13, 2020
Submission Deadline: Aug 31, 2020
Notification of Acceptance: Sep 28, 2020
Camera Ready Version Due : Oct 16, 2020


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.

Call For Paper

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
  • Federated Learning
  • Neural architecture search
  • Deep Learning at the edge

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