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5th Workshop on Accelerated Machine Learning (AccML)
Co-located with the HiPEAC 2023 Conference
(https://www.hipeac.net/2023/toulouse/)
January 18, 2023
Toulouse, France
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CALL FOR CONTRIBUTIONS
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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: https://accml.dcs.gla.ac.uk/
HiPEAC: https://www.hipeac.net/2023/toulouse/#/program/sessions/8030/
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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;
– Software ML acceleration: languages, primitives, libraries, compilers and frameworks;
– 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 the 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 work-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 spark
productive discussions.
The workshop does not have formal proceedings.
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Important Dates
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Submission deadline: November 30, 2022
Notification of decision: December 15, 2022
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Organizers
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José Cano (University of Glasgow)
Valentin Radu (University of Sheffield)
José L. Abellán (Catholic University of Murcia)
Marco Cornero (DeepMind)
Dominik Grewe (DeepMind)
Ulysse Beaugnon (Google)