Call for Papers
We invited submissions of extended abstracts to the Machine Learning for Systems Workshop which was be held in conjunction with NeurIPS 2018 on December 8th 2018.
Overview
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 infrastructure1.
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 welcomed 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 were made available on the workshop website, but there are no formal proceedings. Authors may therefore publish their work in other journals or conferences.
The workshop included invited talks from industry and academia as well as oral and poster presentations by participants.
The workshop had a pool of NeurIPS registrations that was awarded to the authors of accepted submissions.