posted by user: jocare || 1544 views || tracked by 4 users: [display]

2nd AccML 2020 : 2nd Workshop on Accelerated Machine Learning (AccML)

FacebookTwitterLinkedInGoogle

Link: http://workshops.inf.ed.ac.uk/accml/
 
When May 31, 2020 - May 31, 2020
Where Valencia, Spain
Submission Deadline May 1, 2020
Notification Due May 15, 2020
Categories    computer architecture   computer systems   accelerators   machine learning
 

Call For Papers

==================================================================
2nd Workshop on Accelerated Machine Learning (AccML)

Co-located with the ISCA 2020 Conference
(https://iscaconf.org/isca2020/)

May 31, 2020
Valencia, Spain
==================================================================


-------------------------------------------------------------------------
CALL FOR CONTRIBUTIONS
-------------------------------------------------------------------------
In the last 5 years, 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/

ISCA: https://www.iscaconf.org/isca2020/program/workshops.html


-------------------------------------------------------------------------
Invited Speakers
-------------------------------------------------------------------------

- David Kaeli (Northeastern University)

- Antonio González (Universitat Politècnica de Catalunya)

Two additional speakers will be announced before the paper submission deadline.


-------------------------------------------------------------------------
Topics
-------------------------------------------------------------------------
Topics of interest include (but are not limited to):

- Novel ML systems: heterogeneous multi/many-core systems, GPUs, FPGAs;
- 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 a 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 works-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 generally spark discussion.

The workshop does not have formal proceedings.


-------------------------------------------------------------------------
Important Dates
-------------------------------------------------------------------------
Submission deadline: May 1, 2020
Notification of decision: May 15, 2020


-------------------------------------------------------------------------
Organizers
-------------------------------------------------------------------------
José Cano (University of Glasgow)
José L. Abellán (Catholic University of Murcia)
Albert Cohen (Google)
Alex Ramirez (Google)

Related Resources

FAIML 2020-Ei Compendex & Scopus 2020   2020 2nd International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML 2020)
ICDM 2020   20th IEEE International Conference on Data Mining
WSPML 2020   2020 2nd International Workshop on Signal Processing and Machine Learning (WSPML 2020)
IEEE COINS 2020   Internet of Things IoT | Artificial Intelligence | Machine Learning | Big Data | Blockchain | Edge & Cloud Computing | Security | Embedded Systems | Circuit and Systems | WSN | 5G
AICA 2020   O'Reilly AI Conference San Jose
MNLP 2020   4th IEEE Conference on Machine Learning and Natural Language Processing
IEEE-CVIV 2020   2020 2nd International Conference on Advances in Computer Vision, Image and Virtualization (CVIV 2020)
MAAIDL 2020   Springer Book 'Malware Analysis using Artificial Intelligence and Deep Learning'
ACM--ICMLC--Ei and Scopus 2020   ACM--2020 12th International Conference on Machine Learning and Computing (ICMLC 2020)--SCOPUS, Ei Compendex
SI-FDS-MLJ 2021   CFP: Special Issue on Foundations of Data Science - Machine Learning Journal