Emerging Deep Learning Accelerators (EDLA)

In this HiPEAC 2019 workshop we aim to bring together researchers working in Machine Learning and System Architectures to discuss requirements, opportunities, challenges and next steps in developing novel approaches for accelerating deep neural networks.

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HiPEAC 2019 workshop

21st January, 2019

Valencia, Spain

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 aims to enable discussions on emerging acceleration techniques and computation paradigms for deep learning algorithms. The timing of this workshop is ideal, with European regulations tightening data privacy, thus forcing more computations/inferences to be performed at the Edge.

Call For Contributions


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);

  • Software accelerators: primitives, libraries, compilers and frameworks;

Important Dates

  • Submission deadline: November 9th November 23th, 2018 (11:59 PM PDT)

  • Notification to authors: December 18th, 2018

  • Camera-ready deadline: January 10th, 2019

Paper Format

Regular (up to 9 pages) or short (up to 5 pages) paper using SIGCHI Extended Abstract format. Papers should be in PDF format and not anonymized.

Submission Site

Submissions can be made at easychair.org/conferences/?conf=edla2019.

Submission Options

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 keep the following options in mind when preparing submissions:

  • Tentative Research Ideas: Presenting your research idea early one to get feedback and enable collaborations.

  • Works-In-Progress: 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.

Keynote Speaker

Antonio Gonzalez

Antonio González

Director, ARCO Research Group
Professor, Computer Architecture Department
Universitat Politècnica de Catalunya, Barcelona

Title: Milliwatt Human-Quality Speech Recognition


Automatic speech recognition (ASR) will be a key feature for many computing systems, and in particular for mobile devices such as smartphones, tablets, home devices and wearables. For instance, ASR technology is at the heart of popular applications with voice-based user interfaces for mobile devices such as Google Now, Apple Siri, Microsoft Cortana or Amazon Alexa. These systems require support for real-time, large-vocabulary, speaker-independent, highly-accurate, continuous speech recognition. Unfortunately, supporting fast and accurate speech recognition requires a huge computational power, which is especially challenging to attain in devices with very tight constraints in energy consumption. Improvements in energy-efficiency is a key requirement for high-fidelity ASR.

The main driving forces in the past for improving energy-efficiency were based on process technology and microarchitecture innovations in general-purpose processors. However, both of them are reaching a point of diminishing returns. On the other hand, domain-specific architectures offer great potential to keep delivering dramatic improvements in energy-efficiency, and we believe they will become a key ingredient of future computing systems.

In this talk, we will first review the main trends in computing and the state-of-the-art approaches for ASR and then, we will present a novel domain-specific architecture that provides dramatic improvements in terms of energy-efficiency for ASR.


Time Event (in room 5) 21st January 2019
14:00–14:05 Welcome (José Cano, Valentin Radu)
14:05–15:00 Keynote: Milliwatt Human-Quality Speech Recognition (Antonio González)
15:00–15:30 Paper presentations
15:30–16:00 Coffee break
16:00–17:30 Paper presentations
17:30–17:35 Closing remarks (José Cano, Valentin Radu)

Organizers and TPC

José Cano (University of Glasgow)

Valentin Radu (University of Edinburgh)

David Gregg (Trinity College Dublin)

Nuria Pazos (University of Applied Sciences (HES-SO))

Holger Fröning (University of Heidelberg)

Elliot Crowley (University of Edinburgh)

Miguel de Prado (ETH Zurich)

Jack Turner (University of Edinburgh)

Andrew Mundy (ARM Research)

Tim Llewellynn (NVISO)


If you have any questions, please feel free to send an email to edla-info@inf.ed.ac.uk.