ML for Systems Workshop

ML for Systems Workshop
in conjunction with NIPS 2018
Montreal, Canada
December 8, 2018

Submission Deadline: October 19, 2018

Designing specialized hardware for deep learning is a topic that has received significant research attention, both in industrial and academic settings, leading to exponential increases in compute capability in GPUs and accelerators. However, using machine learning to optimize and accelerate software and hardware systems is a lightly explored but promising field, with broad implications for computing as a whole. Very recent work has outlined a broad scope where deep learning vastly outperforms traditional heuristics including topics such as: scheduling, data structure design, microarchitecture, compilers, control of warehouse scale computing systems, and auto-tuned software infrastructure.

The focus of this workshop is to expand upon this recent work and build a community focused on using machine learning in computer systems problems. We seek to improve the state of the art in the areas where learning has already proven to perform better than traditional heuristics, as well as expand to new areas throughout the system stack such as hardware/circuit design and operating/runtime systems.

We welcome submission of up to 4-page extended abstracts in the broad area of using machine learning to accelerate, design, or architect computer systems and software. Accepted papers will be made available on the workshop website, but there will be no formal proceedings. Authors may therefore publish their work in other journals or conferences. The workshop will include invited talks from industry and academia as well as oral and poster presentations by participants.

The workshop has a pool of NIPS registrations that will be awarded to the authors of accepted submissions.

Areas of interest:
– Supervised, unsupervised, and reinforcement learning research with applications to:
– Systems Software
– Runtime Systems
– Distributed Systems
– Security
– Compilers, data structures, and code optimization
– Computer architecture, microarchitecture, and accelerators
– Circuit design and layout
– Interconnects and Networking
– Storage
– Datacenters
– Representation learning for hardware and software
– Optimization of computer systems and software
– Systems modeling and simulation
– Implementations of ML for Systems and challenges
– High quality datasets for ML for Systems problems

SUBMISSION GUIDELINES:
We welcome submissions of up to 4 pages (not including references). This is not a strict limit, but authors are encouraged to adhere to it if possible. All submissions must be in PDF format and should follow the NIPS 2018 format. Submissions do not have to be anonymized.

Please submit your paper no later than October 19th, 2018 to CMT at: https://cmt3.research.microsoft.com/NIPSMLforSystems2018

IMPORTANT DATES:
Submission Deadline: October 19, 2018
Acceptance Notifications: November 9, 2018
Final File Upload: To be Announced
Workshop: December 8th, 2018

Contact us at mlforsystems@googlegroups.com.