Workshop on ML for Systems at ISCA 2019, June 23rd, Phoenix, AZ, USA


Learning Scheduling Algorithms for Data Processing Clusters. Hongzi Mao, Malte Schwarzkopf, Shaileshh Bojja Venkatakrishnan, Zili Meng, and Mohammad Alizadeh.

Generative Adversarial Networks for Clustering Semiconductor Wafer Maps. Hamidreza Mahyar, Elahe Ghalebi, Peter Tulala, and Radu Grusu.

Virtual Address Translation via Learned Page Table Indexes. Artemiy Margaritov, Dmitri Ustiugov, Edouard Bugnion, and Boris Grot.

Exploring the Use of Learning Algorithms for Efficient Performance Profiling. Shoumik Palkar*, Sahaana Suri*, Matei Zaharia, and Peter D. Bailis.

Neural Inference of API Functions from Input–Output Examples. Rohan Bavishi, Caroline Lemieux, Neel Kant, Roy Fox, Koushik Sen, and Ion Stoica. (slides)

A K-means Cluster-Driven Calibration to Improve the Accuracy of Personal Wearable UV Sensors. Thomas Pumir, Emmanuel Dumont, Peter Kaplan, and Shayak Banerjee.

DeepConf: Automating Data Center Network Topologies Management with Machine Learning. Saim Salman, Theophilus Benson, and Asim Kadav.

Cache Miss Rate Predictability via Neural Networks. Rishikesh Jha*, Arjun Karuvally*, Saket Tiwari*, and J. Eliot B. Moss.

Placeto: Efficient Progressive Device Placement Optimization. Ravichandra Addanki, Shaileshh Venkatakrishnan, Shreyan Gupta, Hongzi Mao, and Dr. Mohammad Alizadeh.

Lifting the Curse of Multidimensional Data with Learned Existence Indexes. Stephen Macke, Alex Beutel, Tim Kraska, Maheswaran Sathiamoorthy, Derek Zhiyuan Cheng, and Ed H. Chi.

PeCC: Prediction-error Correcting Cache. Vaishnav Janardhan and Adit Bhardwaj.

Chasing the Signal: Statistically Separating Multi-Tenant I/O Workloads. Si Chen and Avani Wildani.

Iroko: A Framework to Prototype Reinforcement Learning for Data Center Traffic Control. Fabian Ruffy*, Michael Przystupa*, and Ivan Beschastnikh. (slides)

Learning to Optimize Tensor Programs. Tianqi Chen, Lianmin Zheng, Eddie Yan, Ziheng Jiang, Thierry Moreau, Luis Ceze, Carlos Guestrin, and Arvind Krishnamurthy.

Learning to Design Circuits. Hanrui Wang*, Jiacheng Yang*, Hae-Seung Lee, and Song Han.

End-to-end Learning for Distributed Circuit Design. Hao He*, Guo Zhang*, Jack Holloway, and Dina Katabi.

Dali: Lazy Compilation & Kernel Fusion in Dynamic Computation Graphs. Jonathan Raiman.

ReLeQ: An Automatic Reinforcement Learning Approach for Deep Quantization of Neural Networks. Amir Yazdanbakhsh*, Ahmed T. Elthakeb*, Prannoy Pilligundla, Fatemeh Sadat Mireshghallah, and Hadi Esmaeilzadeh.

Automated Testing of Graphics Units by Deep-Learning Detection of Visual Anomalies. Lev Faivishevsky, Ashwin K Muppalla, Ravid Shwartz-Ziv, Ronen Laperdon, Benjamin Melloul, Tahi Hollander, and Amitai Armon.

* Equal Contribution

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