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4th Workshop on Accelerated Machine Learning
(AccML)
Co-located with the HiPEAC 2022 Conference
(https://www.hipeac.net/2022/budapest/)
June 22, 2022
Budapest, Hungary
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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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Link to the Workshop pages
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Organizers: https://accml.dcs.gla.ac.uk/
HiPEAC: https://www.hipeac.net/2022/budapest/#/program/sessions/7919/
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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.
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Important Dates
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Submission deadline: April 15, 2022
Notification of decision: April 30, 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)
Albert Cohen (Google)
Dominik Grewe (DeepMind)