Co-located with the ISCA 2020 Conference
(https://iscaconf.org/isca2020/)

May 31, 2020
Valencia, Spain
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CALL FOR CONTRIBUTIONS
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In the last 5 years, the remarkable performance achieved in a variety of
application areas (natural language processing, computer vision, games,
etc.) has led to the emergence of heterogeneous architectures to
accelerate machine learning workloads. In parallel, production
deployment, model complexity and diversity pushed for higher
productivity systems, more powerful programming abstractions, software
and system architectures, dedicated runtime systems and numerical
libraries, deployment and analysis tools. Deep learning models are
generally memory and computationally intensive, for both training and
inference. 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. In addition, while convolutional neural networks have
motivated much of this effort, numerous applications and models involve
a wider variety of operations, network architectures, and data
processing. These applications and models permanently challenge computer
architecture, the system stack, and programming abstractions. The high
level of interest in these areas calls for a dedicated forum to discuss
emerging acceleration techniques and computation paradigms for machine
learning algorithms, as well as the applications of machine learning to
the construction of such systems.

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Links to the Workshop pages
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Organizers: http://workshops.inf.ed.ac.uk/accml/

ISCA: https://www.iscaconf.org/isca2020/program/workshops.html

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Invited Speakers
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– David Kaeli (Northeastern University)

– Antonio González (Universitat Politècnica de Catalunya)

Two additional speakers will be announced before the paper submission
deadline.

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Topics
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Topics of interest include (but are not limited to):

– Novel ML systems: heterogeneous multi/many-core systems, GPUs, FPGAs;
– Novel ML hardware accelerators and associated software;
– Emerging semiconductor technologies with applications to ML hardware
acceleration;
– ML for the construction and tuning of systems;
– Cloud and edge ML computing: hardware and software to accelerate
training and inference;
– Computing systems research addressing the privacy and security of
ML-dominated systems.

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Submission
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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 accelerating
machine learning. 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.

The workshop does not have formal proceedings.

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Important Dates
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Submission deadline: May 1, 2020
Notification of decision: May 15, 2020

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Organizers
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José Cano (University of Glasgow)
José L. Abellán (Catholic University of Murcia)
Albert Cohen (Google)
Alex Ramirez (Google)