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ML For Systems
Milad Hashemi · Azalia Mirhoseini · Anna Goldie · Kevin Swersky · Xinlei XU · Jonathan Raiman · Jonathan Raiman

Sat Dec 08:00 AM -- 06:00 PM PST @ West 202 - 204
Event URL: http://mlforsystems.org »

Compute requirements are growing at an exponential rate, and optimizing these computer systems often involves complex high-dimensional combinatorial problems. Yet, current methods rely heavily on heuristics. Very recent work has outlined a broad scope where machine learning vastly outperforms these traditional heuristics: including scheduling, data structure design, microarchitecture, compilers, circuit design, and the control of warehouse scale computing systems. In order to continue to scale these computer systems, new learning approaches are needed. The goal of this workshop is to develop novel machine learning methods to optimize and accelerate software and hardware systems. 

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.

This workshop is part two of a two-part series with one day focusing on ML for Systems and the other on Systems for ML. 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.

09:00 AM Opening (Presentation)||
09:10 AM Invited Speaker: Eytan Bakshy (Invited Talk)|| Eytan Bakshy
09:45 AM Break
10:30 AM Poster Session 1 (Poster Session)||
Hongzi Mao, Vikram Nathan, Ioana Baldini, Viswanath Sivakumar, Haonan Wang, Vinoj Jayasundara Magalle Hewa, Zhan Shi, Sam Kaufman, Joyce Fang, Giulio Zhou, Jialin Ding, Hao He, Miles Lubin
11:00 AM Contributed Talk 1: A Weak Supervision Approach to Detecting Visual Anomalies for Automated Testing of Graphics Units (Contributed Talk)|| Adi Szeskin
11:15 AM Contributed Talk 2: Learned TPU Cost Model for XLA Tensor Programs (Contributed Talk)|| Sam Kaufman
11:30 AM Contributed Talk 3: Learned Multi-dimensional Indexing (Contributed Talk)|| Vikram Nathan
11:45 AM Contributed Talk 4: Neural Hardware Architecture Search (Contributed Talk)|| Yujun Lin
12:00 PM Lunch
01:45 PM Invited Speaker: Jeff Dean (Invited Talk)|| Jeff Dean
02:15 PM Invited Speaker: Akanksha Jain (Invited Talk)|| Akanksha Jain
02:45 PM Contributed Talk 5: Predictive Precompute with Recurrent Neural Networks (Contributed Talk)|| Hanson Wang
03:00 PM Poster Session 2 (Poster Session)||
Hanson Wang, Yujun Lin, Yixiao Duan, Aditya Paliwal, Ameer Haj-Ali, Ryan Marcus, Tom Hope, Qiumin Xu, Nham Le, Yuxiang Sun, Ross Cutler, Vikram Nathan, Min Sun
03:30 PM Break
04:15 PM Contributed Talk 6: Zero-Shot Learning for Fast Optimization of Computation Graphs (Contributed Talk)|| Aditya Paliwal
04:30 PM Invited Speaker: Ion Stoica (Invited Talk)|| Ion Stoica
04:55 PM Invited Speaker: Mohammad Alizadeh (Invited Talk)|| Mohammad Alizadeh
05:20 PM Panel (Panel Discussion)||

Author Information

Milad Hashemi (Google)
Azalia Mirhoseini (Google Brain)
Anna Goldie (Google Brain / Stanford)
Kevin Swersky (Google)
Xinlei XU (NYU)
Jonathan Raiman (OpenAI)
Jonathan Raiman (Dali)

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