Proceedings of Machine Learning and Systems 2

MLSys 2020

Edited by I. Dhillon and D. Papailiopoulos and V. Sze

34 papers

Attention-based Learning for Missing Data Imputation in HoloClean Richard Wu, Aoqian Zhang, Ihab Ilyas, Theodoros Rekatsinas
Checkmate: Breaking the Memory Wall with Optimal Tensor Rematerialization Paras Jain, Ajay Jain, Aniruddha Nrusimha, Amir Gholami, Pieter Abbeel, Joseph Gonzalez, Kurt Keutzer, Ion Stoica
Memory-Driven Mixed Low Precision Quantization for Enabling Deep Network Inference on Microcontrollers Manuele Rusci, Alessandro Capotondi, Luca Benini
Trained Quantization Thresholds for Accurate and Efficient Fixed-Point Inference of Deep Neural Networks Sambhav Jain, Albert Gural, Michael Wu, Chris Dick
Federated Optimization in Heterogeneous Networks Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, Virginia Smith
BPPSA: Scaling Back-propagation by Parallel Scan Algorithm Shang Wang, Yifan Bai, Gennady Pekhimenko
Automatically batching control-intensive programs for modern accelerators Alexey Radul, Brian Patton, Dougal Maclaurin, Matthew Hoffman, Rif A. Saurous
MLPerf Training Benchmark Peter Mattson, Christine Cheng, Gregory Diamos, Cody Coleman, Paulius Micikevicius, David Patterson, Hanlin Tang, Gu-Yeon Wei, Peter Bailis, Victor Bittorf, David Brooks, Dehao Chen, Debo Dutta, Udit Gupta, Kim Hazelwood, Andy Hock, Xinyuan Huang, Daniel Kang, David Kanter, Naveen Kumar, Jeffery Liao, Deepak Narayanan, Tayo Oguntebi, Gennady Pekhimenko, Lillian Pentecost, Vijay Janapa Reddi, Taylor Robie, Tom St John, Carole-Jean Wu, Lingjie Xu, Cliff Young, Matei Zaharia
FLEET: Flexible Efficient Ensemble Training for Heterogeneous Deep Neural Networks Hui Guan, Laxmikant Kishor Mokadam, Xipeng Shen, Seung-Hwan Lim, Robert Patton
MotherNets: Rapid Deep Ensemble Learning Abdul Wasay, Brian Hentschel, Yuze Liao, Sanyuan Chen, Stratos Idreos
Predictive Precompute with Recurrent Neural Networks Hanson Wang, Zehui Wang, Yuanyuan Ma
Riptide: Fast End-to-End Binarized Neural Networks Joshua Fromm, Meghan Cowan, Matthai Philipose, Luis Ceze, Shwetak Patel
AutoPhase: Juggling HLS Phase Orderings in Random Forests with Deep Reinforcement Learning Ameer Haj-Ali, Qijing (Jenny) Huang, John Xiang, William Moses, Krste Asanovic, John Wawrzynek, Ion Stoica
A Systematic Methodology for Analysis of Deep Learning Hardware and Software Platforms Yu Wang, Gu-Yeon Wei, David Brooks
Searching for Winograd-aware Quantized Networks Javier Fernandez-Marques, Paul Whatmough, Andrew Mundy, Matthew Mattina
Privacy-Preserving Bandits Mohammad Malekzadeh, Dimitrios Athanasakis, Hamed Haddadi, Ben Livshits
What is the State of Neural Network Pruning? Davis Blalock, Jose Javier Gonzalez Ortiz, Jonathan Frankle, John Guttag
Distributed Hierarchical GPU Parameter Server for Massive Scale Deep Learning Ads Systems Weijie Zhao, Deping Xie, Ronglai Jia, Yulei Qian, Ruiquan Ding, Mingming Sun, Ping Li
Ordering Chaos: Memory-Aware Scheduling of Irregularly Wired Neural Networks for Edge Devices Byung Hoon Ahn, Jinwon Lee, Jamie Menjay Lin, Hsin-Pai Cheng, Jilei Hou, Hadi Esmaeilzadeh
OPTIMUS: OPTImized matrix MUltiplication Structure for Transformer neural network accelerator Junki Park, Hyunsung Yoon, Daehyun Ahn, Jungwook Choi, Jae-Joon Kim
Improving the Accuracy, Scalability, and Performance of Graph Neural Networks with Roc Zhihao Jia, Sina Lin, Mingyu Gao, Matei Zaharia, Alex Aiken
SkyNet: a Hardware-Efficient Method for Object Detection and Tracking on Embedded Systems Xiaofan Zhang, Haoming Lu, Cong Hao, Jiachen Li, Bowen Cheng, Yuhong Li, Kyle Rupnow, Jinjun Xiong, Thomas Huang, Honghui Shi, Wen-Mei Hwu, Deming Chen
A System for Massively Parallel Hyperparameter Tuning Liam Li, Kevin Jamieson, Afshin Rostamizadeh, Ekaterina Gonina, Jonathan Ben-tzur, Moritz Hardt, Benjamin Recht, Ameet Talwalkar
PoET-BiN: Power Efficient Tiny Binary Neurons Sivakumar Chidambaram, Pierre Langlois, Jean-Pierre David
Sense & Sensitivities: The Path to General-Purpose Algorithmic Differentiation Mike Innes
MNN: A Universal and Efficient Inference Engine Xiaotang Jiang, Huan Wang, Yiliu Chen, Ziqi Wu, Lichuan Wang, Bin Zou, Yafeng Yang, Zongyang Cui, Yu Cai, Tianhang Yu, Chengfei Lyu, Zhihua Wu
Understanding the Downstream Instability of Word Embeddings Megan Leszczynski, Avner May, Jian Zhang, Sen Wu, Christopher Aberger, Christopher Re
Resource Elasticity in Distributed Deep Learning Andrew Or, Haoyu Zhang, Michael Freedman
SLIDE : In Defense of Smart Algorithms over Hardware Acceleration for Large-Scale Deep Learning Systems Beidi Chen, Tharun Medini, James Farwell, sameh gobriel, Charlie Tai, Anshumali Shrivastava
Blink: Fast and Generic Collectives for Distributed ML Guanhua Wang, Shivaram Venkataraman, Amar Phanishayee, Nikhil Devanur, Jorgen Thelin, Ion Stoica
Fine-Grained GPU Sharing Primitives for Deep Learning Applications Peifeng Yu, Mosharaf Chowdhury
Willump: A Statistically-Aware End-to-end Optimizer for Machine Learning Inference Peter Kraft, Daniel Kang, Deepak Narayanan, Shoumik Palkar, Peter Bailis, Matei Zaharia
Model Assertions for Monitoring and Improving ML Models Daniel Kang, Deepti Raghavan, Peter Bailis, Matei Zaharia
PLink: Discovering and Exploiting Locality for Accelerated Distributed Training on the public Cloud Liang Luo, Peter West, Jacob Nelson, Arvind Krishnamurthy, Luis Ceze

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