Table of Contents

• SLIDE : In Defense of Smart Algorithms over Hardware Acceleration for Large-Scale Deep Learning SystemsBeidi Chen, Tharun Medini, James Farwell, Sameh Gobriel, Tsung-Yuan Charlie Tai, Anshumali Shrivastava. [doi]
• Distributed Hierarchical GPU Parameter Server for Massive Scale Deep Learning Ads SystemsWeijie Zhao, Deping Xie, Ronglai Jia, Yulei Qian, Ruiquan Ding, Mingming Sun, Ping Li 0001. [doi]
• Riptide: Fast End-to-End Binarized Neural NetworksJoshua Fromm, Meghan Cowan, Matthai Philipose, Luis Ceze, Shwetak Patel. [doi]
• MNN: A Universal and Efficient Inference EngineXiaotang Jiang, Huan Wang, Yiliu Chen, Ziqi Wu, Lichuan Wang, Bin Zou, Yafeng Yang, Zongyang Cui, Yu Cai, Tianhang Yu, Chengfei Lyu, Zhihua Wu. [doi]
• MotherNets: Rapid Deep Ensemble LearningAbdul Wasay, Brian Hentschel, Yuze Liao, Sanyuan Chen, Stratos Idreos. [doi]
• Blink: Fast and Generic Collectives for Distributed MLGuanhua Wang, Shivaram Venkataraman, Amar Phanishayee, Jorgen Thelin, Nikhil R. Devanur, Ion Stoica. [doi]
• Checkmate: Breaking the Memory Wall with Optimal Tensor RematerializationParas Jain 0001, Ajay Jain, Aniruddha Nrusimha, Amir Gholami, Pieter Abbeel, Kurt Keutzer, Ion Stoica, Joseph Gonzalez 0001. [doi]
• AutoPhase: Juggling HLS Phase Orderings in Random Forests with Deep Reinforcement LearningAmeer Haj Ali, Qijing (Jenny) Huang, William Moses, John Xiang, Krste Asanovic, John Wawrzynek, Ion Stoica. [doi]
• Federated Optimization in Heterogeneous NetworksTian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, Virginia Smith. [doi]
• FLEET: Flexible Efficient Ensemble Training for Heterogeneous Deep Neural NetworksHui Guan, Laxmikant Kishor Mokadam, Xipeng Shen, Seung-Hwan Lim, Robert M. Patton. [doi]
• MLPerf Training BenchmarkPeter Mattson, Christine Cheng, Gregory F. Diamos, Cody Coleman, Paulius Micikevicius, David A. Patterson, Hanlin Tang, Gu-Yeon Wei, Peter Bailis, Victor Bittorf, David Brooks 0001, Dehao Chen, Debo Dutta, Udit Gupta, Kim M. 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. [doi]
• Searching for Winograd-aware Quantized NetworksJavier Fernández-Marqués, Paul N. Whatmough, Andrew Mundy, Matthew Mattina. [doi]
• Willump: A Statistically-Aware End-to-end Optimizer for Machine Learning InferencePeter Kraft, Daniel Kang, Deepak Narayanan, Shoumik Palkar, Peter Bailis, Matei Zaharia. [doi]
• A System for Massively Parallel Hyperparameter TuningLiam Li, Kevin G. Jamieson, Afshin Rostamizadeh, Ekaterina Gonina, Jonathan Ben-tzur, Moritz Hardt, Benjamin Recht, Ameet Talwalkar. [doi]
• Attention-based Learning for Missing Data Imputation in HoloCleanRichard Wu, Aoqian Zhang, Ihab F. Ilyas, Theodoros Rekatsinas. [doi]
• Trained Quantization Thresholds for Accurate and Efficient Fixed-Point Inference of Deep Neural NetworksSambhav R. Jain, Albert Gural, Michael Wu, Chris Dick. [doi]
• Model Assertions for Monitoring and Improving ML ModelsDaniel Kang, Deepti Raghavan, Peter Bailis, Matei Zaharia. [doi]
• Fine-Grained GPU Sharing Primitives for Deep Learning ApplicationsPeifeng Yu, Mosharaf Chowdhury. [doi]
• What is the State of Neural Network Pruning?Davis W. Blalock, Jose Javier Gonzalez Ortiz, Jonathan Frankle, John V. Guttag. [doi]
• Improving the Accuracy, Scalability, and Performance of Graph Neural Networks with RocZhihao Jia, Sina Lin, Mingyu Gao, Matei Zaharia, Alex Aiken. [doi]
• PoET-BiN: Power Efficient Tiny Binary NeuronsSivakumar Chidambaram, Pierre Langlois 0001, Jean-Pierre David. [doi]
• PLink: Discovering and Exploiting Locality for Accelerated Distributed Training on the public CloudLiang Luo, Peter West, Arvind Krishnamurthy, Luis Ceze, Jacob Nelson. [doi]
• A Systematic Methodology for Analysis of Deep Learning Hardware and Software PlatformsYu Wang, Gu-Yeon Wei, David Brooks 0001. [doi]
• SkyNet: a Hardware-Efficient Method for Object Detection and Tracking on Embedded SystemsXiaofan Zhang, Haoming Lu, Cong Hao, Jiachen Li, Bowen Cheng, Yuhong Li, Kyle Rupnow, Jinjun Xiong, Thomas S. Huang, Honghui Shi, Wen-mei Hwu, Deming Chen. [doi]
• Ordering Chaos: Memory-Aware Scheduling of Irregularly Wired Neural Networks for Edge DevicesByung Hoon Ahn, Jinwon Lee, Jamie Menjay Lin, Hsin-Pai Cheng, Jilei Hou, Hadi Esmaeilzadeh. [doi]
• Predictive Precompute with Recurrent Neural NetworksHanson Wang, Zehui Wang, Yuanyuan Ma. [doi]
• Memory-Driven Mixed Low Precision Quantization for Enabling Deep Network Inference on MicrocontrollersManuele Rusci, Alessandro Capotondi, Luca Benini. [doi]
• Automatically batching control-intensive programs for modern acceleratorsAlexey Radul, Brian Patton, Dougal Maclaurin, Matthew D. Hoffman, Rif A. Saurous. [doi]
• Privacy-Preserving BanditsMohammad Malekzadeh, Dimitrios Athanasakis, Hamed Haddadi, Benjamin Livshits. [doi]
• OPTIMUS: OPTImized matrix MUltiplication Structure for Transformer neural network acceleratorJunki Park, Hyunsung Yoon, Daehyun Ahn, Jungwook Choi, Jae-Joon Kim. [doi]
• Resource Elasticity in Distributed Deep LearningAndrew Or, Haoyu Zhang, Michael J. Freedman. [doi]
• Understanding the Downstream Instability of Word EmbeddingsMegan Leszczynski, Avner May, Jian Zhang, Sen Wu 0002, Christopher R. Aberger, Christopher Ré. [doi]
• Sense & Sensitivities: The Path to General-Purpose Algorithmic DifferentiationMike Innes. [doi]
• BPPSA: Scaling Back-propagation by Parallel Scan AlgorithmShang Wang 0002, Yifan Bai, Gennady Pekhimenko. [doi]