MLSys 2020 Accepted Papers 34

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MNN: A Universal and Efficient Inference Engine
Xiaotang Jiang (Alibaba) · Huan Wang (Northeastern University) · Yiliu Chen (Alibaba Group) · Ziqi Wu (Alibaba Group) · Lichuan Wang (Alibaba Group) · Bin Zou (alibaba) · Yafeng Yang (Alibaba Group) · Zongyang Cui (Alibaba Group) · Yu Cai (Alibaba Group) · Tianhang Yu (Alibaba Group) · Chengfei Lyu (Alibaba Group) · Zhihua Wu (Alibaba)

Searching for Winograd-aware Quantized Networks
Javier Fernandez-Marques (University of Oxford) · Paul Whatmough (Arm ML Research Lab) · Andrew Mundy (Arm ML Research Lab) · Matthew Mattina (Arm ML Research Lab)

A Systematic Methodology for Analysis of Deep Learning Hardware and Software Platforms
Yu Wang (Harvard University) · Gu-Yeon Wei (Harvard University) · David Brooks (Harvard University)

Ordering Chaos: Memory-Aware Scheduling of Irregularly Wired Neural Networks for Edge Devices
Byung Hoon Ahn (UC San Diego) · Jinwon Lee (Qualcomm AI Research) · Jamie Menjay Lin (Qualcomm AI Research) · Hsin-Pai Cheng (Duke University) · Jilei Hou (Qualcomm AI Research) · Hadi Esmaeilzadeh (University of California, San Diego)

Sense & Sensitivities: The Path to General-Purpose Algorithmic Differentiation
Mike Innes (Julia Computing)

AutoPhase: Juggling HLS Phase Orderings in Random Forests with Deep Reinforcement Learning
Ameer Haj-Ali (UC Berkeley) · Qijing (Jenny) Huang (Berkeley) · John Xiang (UC Berkeley) · William Moses (MIT) · Krste Asanovic (UC Berkeley) · John Wawrzynek (UC Berkeley) · Ion Stoica (UC Berkeley)

PLink: Discovering and Exploiting Locality for Accelerated Distributed Training on the public Cloud
Liang Luo (University of Washington) · Peter West (University of Washington) · Jacob Nelson (Microsoft Research) · Arvind Krishnamurthy (University of Washington) · Luis Ceze (University of Washington and OctoML)

Fine-Grained GPU Sharing Primitives for Deep Learning Applications
Peifeng Yu (University of Michigan) · Mosharaf Chowdhury (University of Michigan, Ann Arbor)

Trained Quantization Thresholds for Accurate and Efficient Fixed-Point Inference of Deep Neural Networks
Sambhav R. Jain (Xilinx / Stanford) · Albert Gural (Stanford University) · Michael Wu (Xilinx, Inc.) · Chris Dick (Xilinx, Inc.)

What is the State of Neural Network Pruning?
Davis Blalock (MIT) · Jose Javier Gonzalez Ortiz (MIT) · Jonathan Frankle (MIT) · John Guttag (MIT)

Willump: A Statistically-Aware End-to-end Optimizer for Machine Learning Inference
Peter Kraft (Stanford University) · Daniel Kang (Stanford University) · Deepak Narayanan (Stanford) · Shoumik Palkar (Stanford) · Peter Bailis (Stanford University) · Matei Zaharia (Stanford and Databricks)

PoET-BiN: Power Efficient Tiny Binary Neurons
Sivakumar Chidambaram (Polytechnique Montreal) · Pierre Langlois (Polytechnique Mointreal) · Jean-Pierre David (Polytechnique Montreal)

Blink: Fast and Generic Collectives for Distributed ML
Guanhua Wang (UC Berkeley) · Shivaram Venkataraman (University of Wisconsin, Madison) · Amar Phanishayee (Microsoft Research) · Nikhil Devanur (Microsoft) · Jorgen Thelin (Microsoft Research) · Ion Stoica (UC Berkeley)

Improving the Accuracy, Scalability, and Performance of Graph Neural Networks with Roc
Zhihao Jia (Stanford University) · Sina Lin (Microsoft) · Mingyu Gao (Tsinghua University) · Matei Zaharia (Stanford and Databricks) · Alex Aiken (Stanford University)

MotherNets: Rapid Deep Ensemble Learning
Abdul Wasay (Harvard University) · Brian Hentschel (Harvard University) · Yuze Liao (Harvard University) · Sanyuan Chen (Harvard) · Stratos Idreos (Harvard)

SkyNet: a Hardware-Efficient Method for Object Detection and Tracking on Embedded Systems
Xiaofan Zhang (University of Illinois at Urbana and Champaign) · Haoming Lu (University of Illinois at Urbana and Champaign) · Cong Hao (University of Illinois at Urbana-Champaign) · Jiachen Li (UIUC) · Bowen Cheng (UIUC) · Yuhong Li (University of Illinois at Urbana and Champaign) · Kyle Rupnow (Inspirit IoT, Inc.) · Jinjun Xiong (IBM Thomas J. Watson Research Center) · Thomas Huang (UIUC) · Honghui Shi (IBM | UIUC | Oregon) · Wen-Mei Hwu (University of Illinois at Urbana-Champaign) · Deming Chen (University of Illinois at Urbana-Champaign)

A System for Massively Parallel Hyperparameter Tuning
Liam Li (Carnegie Mellon University) · Kevin Jamieson (U Washington) · Afshin Rostamizadeh (Google Research) · Ekaterina Gonina (Google) · Jonathan Ben-tzur (Determined AI) · Moritz Hardt (UC Berkeley) · Benjamin Recht (UC Berkeley) · Ameet Talwalkar (CMU)

FLEET: Flexible Efficient Ensemble Training for Heterogeneous Deep Neural Networks
Hui Guan (North Carolina State University) · Laxmikant Kishor Mokadam (North Carolina State University) · Xipeng Shen (North Carolina State University) · Seung-Hwan Lim (Oak Ridge National Laboratory) · Robert Patton (Rensselaer Polytechnic Institute, Oak Ridge National Laboratory)

Understanding the Downstream Instability of Word Embeddings
Megan Leszczynski (Stanford University) · Avner May (Stanford University) · Jian Zhang (Stanford University) · Sen Wu (Stanford University) · Christopher Aberger (SambaNova Systems and Stanford University) · Christopher Re (Stanford University)

SLIDE : Training Deep Neural Networks with Large Outputs on a CPU faster than a V100-GPU
Beidi Chen (Rice University) · Tharun Medini (Rice University) · James Farwell (Intel Corporation) · sameh gobriel () · Charlie Tai (Intel Corporation) · Anshumali Shrivastava (Rice University)

Attention-based Learning for Missing Data Imputation in HoloClean
Richard Wu (University of Waterloo) · Aoqian Zhang (University of Waterloo) · Ihab Ilyas (U. of Waterloo) · Theodoros Rekatsinas (University of Wisconsin-Madison)

Memory-Driven Mixed Low Precision Quantization for Enabling Deep Network Inference on Microcontrollers
Manuele Rusci (Universit√† di Bologna) · Alessandro Capotondi (Universit√† di Modena e Reggio Emilia) · Luca Benini (ETHZ)

MLPerf Training Benchmark
Peter Mattson (Google) · Christine Cheng (Intel) · Gregory Diamos (Baidu) · Cody Coleman (Stanford) · Paulius Micikevicius (NVIDIA) · David Patterson (Google) · Hanlin Tang (Intel Corporation) · Gu-Yeon Wei () · Peter Bailis (Stanford University) · Victor Bittorf (Google) · David Brooks (Harvard University) · Dehao Chen (Google) · Debo Dutta (Cisco Systems, Inc.) · Udit Gupta (Harvard University) · Kim Hazelwood (Facebook AI) · Andy Hock (Cerebras Systems) · Xinyuan Huang (Cisco Systems, Inc.) · Daniel Kang (Stanford University) · David Kanter (RWI) · Naveen Kumar (Google) · Jeffery Liao (Synopsys) · Deepak Narayanan (Stanford) · Tayo Oguntebi (Google LLC) · Gennady Pekhimenko (University of Toronto) · Lillian Pentecost (Harvard University) · Vijay Janapa Reddi (Harvard University) · Taylor Robie (Google) · Tom St John (Tesla) · Carole-Jean Wu (Facebook AI) · Lingjie Xu (Alibaba) · Cliff Young (google.com) · Matei Zaharia (Stanford and Databricks)

Privacy-Preserving Bandits
Mohammad Malekzadeh (Queen Mary University of London) · Dimitrios Athanasakis (Brave Software) · Hamed Haddadi (Brave Software) · Ben Livshits (Brave Software)

OPTIMUS: OPTImized matrix MUltiplication Structure for Transformer neural network accelerator
Junki Park (POSTECH) · Hyunsung Yoon (POSTECH) · Daehyun Ahn (POSTECH) · Jungwook Choi (Hanyang University) · Jae-Joon Kim (POSTECH)

Riptide: Fast End-to-End Binarized Neural Networks
Joshua Fromm (University of Washington) · Meghan Cowan (University of Washington) · Matthai Philipose (Microsoft Research) · Luis Ceze (University of Washington and OctoML) · Shwetak Patel (University of Washington)

Automatically batching control-intensive programs for modern accelerators
Alexey Radul (Google) · Brian Patton (Google Inc.) · Dougal Maclaurin (Google Inc.) · Matthew Hoffman (Google) · Rif A. Saurous (Google)

Resource Elasticity in Distributed Deep Learning
Andrew Or (Princeton University) · Haoyu Zhang (Google AI) · Michael Freedman (Princeton University)

Distributed Hierarchical GPU Parameter Server for Massive Scale Deep Learning Ads Systems
Weijie Zhao (Baidu Research) · Deping Xie (Baidu) · Ronglai Jia (Baidu) · Yulei Qian (Baidu) · Ruiquan Ding (Baidu) · Mingming Sun (Baidu Research) · Ping Li (Baidu Research)

Federated Optimization in Heterogeneous Networks
Tian Li (Carnegie Mellon University) · Anit Kumar Sahu (Bosch Center for Artificial Intelligence) · Manzil Zaheer (Google) · Maziar Sanjabi (USC) · Ameet Talwalkar (CMU) · Virginia Smith (Carnegie Mellon University)

BPPSA: Scaling Back-propagation by Parallel Scan Algorithm
Shang Wang (University of Toronto) · Yifan Bai (University of California, Berkeley) · Gennady Pekhimenko (University of Toronto)

Predictive Precompute with Recurrent Neural Networks
Hanson Wang (Facebook) · Zehui Wang (Facebook) · Yuanyuan Ma (Facebook)

Model Assertions for Monitoring and Improving ML Models
Daniel Kang (Stanford University) · Deepti Raghavan (Stanford University) · Peter Bailis (Stanford University) · Matei Zaharia (Stanford and Databricks)

Breaking the Memory Wall with Optimal Tensor Rematerialization
Paras Jain (UC Berkeley) · Ajay Jain (UC Berkeley) · Aniruddha Nrusimha (UC Berkeley) · Amir Gholami (UC Berkeley) · Pieter Abbeel (UC Berkeley) · Joseph Gonzalez (UC Berkeley) · Kurt Keutzer (EECS, UC Berkeley) · Ion Stoica (UC Berkeley)