Workshop on ML for Systems at ISCA 2019, June 23rd
Workshop on ML for Systems at NIPS 2018 Date December 8th, 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 rather a less-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: scheduling1, data structure design2, microarchitecture3, compilers4, control of warehouse scale computing systems, and auto-tuned software infrastructure5.

The main objective of this workshop is to expand upon this recent work and build a community focused on using machine learning in computer architecture and 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.

Call for Papers

Machine Learning for Systems is an interdisciplinary workshop that brings together researchers in computer architecture and systems and machine learning. This workshop is meant to serve as a platform to promote discussions between researchers in the workshops target areas.

We invite 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.

Areas of interest:

  • Supervised, unsupervised, and reinforcement learning research with applications to:
    • Computer architecture, microarchitecture, and accelerators
    • Hardware-software co-design
    • Security
    • Compilers, data structures, and code optimization
    • Reconfigurable architecture
    • Systems software
    • Runtime systems
    • Distributed systems
    • Storage
    • Datacenters
    • Circuit design and layout
    • Interconnects and Networking
  • Representation learning for hardware and software
  • Optimization of computer architecture and systems
  • Systems modeling and simulation
  • Implementations of ML for Systems and challenges

Submission Instructions

  • 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 ISCA'19 Latex Template.
  • Please follow the guidelines provided at ISCA 2019 Paper Submission Guidelines.
  • Submissions must be anonymized for double-blind review.
  • Please submit your paper no later than June 7th, 2019 - Midnight Anywhere On Earth here.

Important Dates

  • Submission Deadline: June 7th, 2019 - Midnight Anywhere on Earth.
  • Acceptance Notifications: June 14th, 2019.
  • Workshop: June 23rd, 2019.

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