Workshop on Emerging Deep Learning Accelerators

1st Workshop on Emerging Deep Learning Accelerators (EDLA)
co-located with HiPEAC 2019
January 21-23, 2019
Valencia, Spain

IMPORTANT DATES:
Submission Deadline: November 9, 2018
Notification of Acceptance: December 7, 2018
Camera-ready Deadline: until December 20, 2018

Deep Learning is receiving much attention these days due to remarkable performance achieved in several fields (e.g. Computer Vision, Speech, Translations, etc), although this brings some challenges to hardware architects and computation optimization researchers. Deep Learning models are generally very large in memory and require many computation instructions to train and perform inferences. Accelerating these operations has obvious advantages, first by reducing the energy consumption (e.g. in data centers) and secondly, making these models usable on smaller devices at the edge of the Internet. This workshop on Emerging Deep Learning Accelerators (EDLA) is intended to bring together researchers from academia and industry to discuss requirements, opportunities, challenges and next steps in developing novel approaches for accelerating deep neural networks. The timing of this workshop is ideal, with European regulations tightening data privacy, thus forcing more computations/inferences to be performed at the Edge.

Topics of interest include (but are not limited to):
– Novel parallel computing architectures​: GPUs, FPGAs, and heterogeneous multi/many-core designs.
– Crazy architectural ideas: focused on accelerating deep learning workloads/algorithms.
– Cloud and edge computing​: hardware and software methods focused on accelerating both training (cloud) and inference (edge).
– Compilers, tools, and programming models: focused on accelerating deep learning workloads/algorithms.

SUBMISSION GUIDELINES:
Papers will be reviewed by the workshop’s technical program committee according to criteria regarding a submission’s quality, relevance to the workshop’s topics, and, foremost, its potential to spark discussions about directions, insights, and solutions in the context of deep learning accelerators. Research papers, case studies, and position papers are all welcome.

In particular, we encourage authors to submit works-In-Progress papers: To facilitate sharing of thought-provoking ideas and high-potential though preliminary research, authors are welcome to make submissions describing early-stage, in-progress, and/or exploratory work in order to elicit feedback, discover collaboration opportunities, and generally spark discussion.

ORGANIZERS:
José Cano – University of Glasgow
Valentin Radu – University of Edinburgh
David Gregg – Trinity College Dublin
Nuria Pazos – University of Applied Sciences (HES-SO)
Elliot Crowley – University of Edinburgh
Miguel de Prado – ETH Zurich
Jack Turner – University of Edinburgh
Andrew Mundy – ARM Research
Tim Llewellynn – NVISO