This workshop is part two of a two-part series with one day focusing on Machine Learning for Systems and the other on Systems for Machine Learning. Although the two workshops are being led by different organizers, we are coordinating our call for papers to ensure that the workshops complement each other and that submitted papers are routed to the appropriate venue.

The Systems for Machine Learning workshop focuses on designing systems to enable ML, whereas we focus on developing ML to optimize systems. Both fields are mature enough to warrant a dedicated workshop. Organizers on both sides are open to merging in the future, but this year we plan to run them separately on two different days.

Designing specialized hardware and systems 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 [1], data structure design [2], microarchitecture [3], compilers [4], and control of warehouse scale computing systems [5].

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.

By forming a community of academic and industrial researchers who are excited about this area, we seek to build towards intelligent, self optimizing systems and answer questions such as: How do we generate and share high quality datasets that span the layers of the system stack? Which learned representations best represent code performance and runtime? Which simulators and simulation methodologies provide a tractable proving ground for techniques like reinforcement learning?

To this end, the target audience for this workshop includes a wide variety of attendees from state-of-the-art researchers in machine learning to domain experts in computer systems design. We have invited a broad set of expert speakers to present the potential for impact of combining machine learning research with computer systems. We hope that providing a formal venue for researchers from both fields to meet and interact will push forward both fundamental research in ML as well as real-world impact to computer systems design and implementation.

The workshop will host 6 speakers/panelists (all confirmed) and we will put out a call for researchers to submit relevant papers, up to 4 pages in the default NIPS style, that will undergo a peer review process. Selected works will be presented as spotlights, contributed talks and/or posters. Speakers will be invited to participate in an interactive panel discussion to conclude the workshop.

The organizers of this workshop span core research in machine learning, computer systems and architecture, as well as their intersection. Jointly, they have published in top-tier systems and machine learning conferences including: NIPS, ICML, ICLR, ISCA, MICRO, DAC, and SIGMETRICS.

References:
[1] Device Placement Optimization with Reinforcement Learning, https://arxiv.org/pdf/1706.04972.pdf
[2] The Case for Learned Index Structures, https://arxiv.org/abs/1712.01208
[3] Learning Memory Access Patterns, https://arxiv.org/pdf/1803.02329.pdf
[4] End to End Deep Learning of Optimization Heuristics: https://ieeexplore.ieee.org/document/8091247/?reload=true
[5] https://deepmind.com/blog/deepmind-ai-reduces-google-data-centre-cooling-bill-40/
[6] Bayesian optimization for tuning the JVM, https://www.youtube.com/watch?v=YhNl468S8CI
[7] Safe Exploration for Identifying Linear Systems via Robust Optimization: https://arxiv.org/abs/1711.11165