[hpc-announce] CFP - 4th Workshop on Accelerated Machine Learning (AccML) at HiPEAC 2022

[Jose Cano Reyes]

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4th Workshop on Accelerated Machine Learning (AccML)


Co-located with the HiPEAC 2022 Conference

(https://www.hipeac.net/2022/budapest/)


January 19, 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 page

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https://accml.dcs.gla.ac.uk/


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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: November 30, 2021

Notification of decision: December 15, 2021


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Organizers

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José Cano (University of Glasgow)

Valentin Radu (University of Sheffield)

José L. Abellán (Universidad Católica de Murcia)

Marco Cornero (DeepMind)

Albert Cohen (Google)

Dominik Grewe (DeepMind)

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