posted by user: jocare || 3511 views || tracked by 2 users: [display]

3rd AccML 2021 : 3rd Workshop on Accelerated Machine Learning (AccML)

FacebookTwitterLinkedInGoogle

Link: http://workshops.inf.ed.ac.uk/accml/
 
When Jan 18, 2021 - Jan 18, 2021
Where Virtual Event
Submission Deadline Nov 30, 2020
Notification Due Dec 15, 2020
Categories    computer architecture   computer systems   accelerators   machine learning
 

Call For Papers

==================================================================
3rd Workshop on Accelerated Machine Learning (AccML)

Co-located with the HiPEAC 2021 Conference
(https://www.hipeac.net/2021/spring-virtual/)

January 18, 2021
Virtual Event
==================================================================

-------------------------------------------------------------------------
UPDATES
-------------------------------------------------------------------------

DEADLINE EXTENDED TO NOVEMBER 30, 2020

NEW INVITED SPEAKER ANNOUNCED

-------------------------------------------------------------------------
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.

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

HiPEAC: https://www.hipeac.net/2021/spring-virtual/#/program/sessions/7837/

-------------------------------------------------------------------------
Invited Speakers
-------------------------------------------------------------------------
- Jem Davies (ARM)
- Vivienne Sze (MIT)
- David Lacey (Graphcore)
- Danilo Pau (STMicroelectronics)

-------------------------------------------------------------------------
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.

The workshop does not have formal proceedings.

-------------------------------------------------------------------------
Important Dates
-------------------------------------------------------------------------
Submission deadline: November 30, 2020
Notification of decision: December 15, 2020

-------------------------------------------------------------------------
Organizers
-------------------------------------------------------------------------
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)
Alex Ramirez (Google)

Related Resources

Federated Learning in IOT Cybersecurity 2021   PeerJ Computer Science - Federated Learning for Cybersecurity in Internet of Things
CVPR 2022   Computer Vision and Pattern Recognition
FAIML 2022   2022 International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML 2022)
ICADCML 2022   3rd International Conference on Advances in Distributed Computing and Machine Learning - 2022
MLDM 2022   18th International Conference on Machine Learning and Data Mining
CFDSP 2022   2022 International Conference on Frontiers of Digital Signal Processing (CFDSP 2022)
ICWSM 2022   International Conference on Web and Social Media (3rd deadline)
Computer SI on SE4RAI 2023   IEEE Computer - Special Issue on Software Engineering for Responsible AI
DLIS 2022   Deep Learning for IoT Security - Frontiers in Big Data Journal
DASFAA 2022   Database Systems for Advanced Applications