==================================================================
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
==================================================================
————————————————————————-
CALL FOR CONTRIBUTIONS
————————————————————————-
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.
————————————————————————-
Link to the Workshop pages
————————————————————————-
Organizers: https://accml.dcs.gla.ac.uk/
HiPEAC: https://www.hipeac.net/2022/budapest/#/program/sessions/7919/
————————————————————————-
Topics
————————————————————————-
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.
————————————————————————-
Submission
————————————————————————-
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.
————————————————————————-
Important Dates
————————————————————————-
Submission deadline: April 30, 2022
Notification of decision: May 16, 2022
————————————————————————-
Organizers
————————————————————————-
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)