- Hang Qiu, Ioanna Vavelidou, Jian Li, Evgenya Pergament, Pete Warden, Sandeep Chinchali, Zain Asgar, Sachin Katti:
ML-EXray: Visibility into ML Deployment on the Edge. MLSys 2022 - Vijay Janapa Reddi, David Kanter, Peter Mattson, Jared Duke, Thai Nguyen, Ramesh Chukka, Kenneth Shiring, Koan-Sin Tan, Mark Charlebois, William Chou, Mostafa El-Khamy, Jungwook Hong, Tom St. John, Cindy Trinh, Michael Buch, Mark Mazumder, Relja Markovic, Thomas Atta-fosu, Fatih Çakir, Masoud Charkhabi, Xiaodong Chen, Cheng-Ming Chiang, Dave Dexter, Terry Heo, Guenther Schmuelling, Maryam Shabani, Dylan Zika:
MLPerf Mobile Inference Benchmark: An Industry-Standard Open-Source Machine Learning Benchmark for On-Device AI. MLSys 2022 - James K. Reed, Zachary DeVito, Horace He, Ansley Ussery, Jason Ansel:
torch.fx: Practical Program Capture and Transformation for Deep Learning in Python. MLSys 2022 - Hippolyt Ritter, Theofanis Karaletsos:
TyXe: Pyro-based Bayesian neural nets for Pytorch. MLSys 2022 - Jiwon Seo:
Gyro Dropout: Maximizing Ensemble Effect in Neural Network Training. MLSys 2022 - Jinhyun So, Corey J. Nolet, Chien-Sheng Yang, Songze Li, Qian Yu, Ramy E. Ali, Basak Guler, Salman Avestimehr:
LightSecAgg: a Lightweight and Versatile Design for Secure Aggregation in Federated Learning. MLSys 2022 - Samuel Alexander Stein, Betis Baheri, Daniel Chen, Ying Mao, Qiang Guan, Ang Li, Shuai Xu, Caiwen Ding:
QuClassi: A Hybrid Deep Neural Network Architecture based on Quantum State Fidelity. MLSys 2022 - Haotian Tang, Zhijian Liu, Xiuyu Li, Yujun Lin, Song Han:
TorchSparse: Efficient Point Cloud Inference Engine. MLSys 2022 - Meet P. Vadera, Jinyang Li, Adam D. Cobb, Brian Jalaian, Tarek F. Abdelzaher, Benjamin M. Marlin:
URSABench: A System for Comprehensive Benchmarking of Bayesian Deep Neural Network Models and Inference methods. MLSys 2022 - Cheng Wan, Youjie Li, Ang Li, Nam Sung Kim, Yingyan Lin:
BNS-GCN: Efficient Full-Graph Training of Graph Convolutional Networks with Partition-Parallelism and Random Boundary Node Sampling. MLSys 2022 - Jaeyeon Won, Jeyeon Si, Sam Son, Tae Jun Ham, Jae W. Lee:
ULPPACK: Fast Sub-8-bit Matrix Multiply on Commodity SIMD Hardware. MLSys 2022 - Carole-Jean Wu, Ramya Raghavendra, Udit Gupta, Bilge Acun, Newsha Ardalani, Kiwan Maeng, Gloria Chang, Fiona Aga Behram, Jinshi Huang, Charles Bai, Michael Gschwind, Anurag Gupta, Myle Ott, Anastasia Melnikov, Salvatore Candido, David Brooks, Geeta Chauhan, Benjamin Lee, Hsien-Hsin S. Lee, Bugra Akyildiz, Maximilian Balandat, Joe Spisak, Ravi Jain, Mike Rabbat, Kim M. Hazelwood:
Sustainable AI: Environmental Implications, Challenges and Opportunities. MLSys 2022 - Ningning Xie, Tamara Norman, Dominik Grewe, Dimitrios Vytiniotis:
Synthesizing Optimal Parallelism Placement and Reduction Strategies on Hierarchical Systems for Deep Learning. MLSys 2022 - Xinfeng Xie, Prakash Prabhu, Ulysse Beaugnon, Phitchaya Mangpo Phothilimthana, Sudip Roy, Azalia Mirhoseini, Eugene Brevdo, James Laudon, Yanqi Zhou:
A Transferable Approach for Partitioning Machine Learning Models on Multi-Chip-Modules. MLSys 2022 - Zhiqiang Xie, Minjie Wang, Zihao Ye, Zheng Zhang, Rui Fan:
Graphiler: Optimizing Graph Neural Networks with Message Passing Data Flow Graph. MLSys 2022 - Jiarong Xing, Leyuan Wang, Shang Zhang, Jack Chen, Ang Chen, Yibo Zhu:
Bolt: Bridging the Gap between Auto-tuners and Hardware-native Performance. MLSys 2022 - Zirui Xu, Fuxun Yu, Jinjun Xiong, Xiang Chen:
QuadraLib: A Performant Quadratic Neural Network Library for Architecture Optimization and Design Exploration. MLSys 2022 - Hengrui Zhang, Zhongming Yu, Guohao Dai, Guyue Huang, Yufei Ding, Yuan Xie, Yu Wang:
Understanding GNN Computational Graph: A Coordinated Computation, IO, and Memory Perspective. MLSys 2022 - Bojian Zheng, Ziheng Jiang, Cody Hao Yu, Haichen Shen, Joshua Fromm, Yizhi Liu, Yida Wang, Luis Ceze, Tianqi Chen, Gennady Pekhimenko:
DietCode: Automatic Optimization for Dynamic Tensor Programs. MLSys 2022 - Yanqi Zhou, Xuanyi Dong, Tianjian Meng, Mingxing Tan, Berkin Akin, Daiyi Peng, Amir Yazdanbakhsh, Da Huang, Ravi Narayanaswami, James Laudon:
Towards the Co-design of Neural Networks and Accelerators. MLSys 2022 - Donglin Zhuang, Xingyao Zhang, Shuaiwen Song, Sara Hooker:
Randomness in Neural Network Training: Characterizing the Impact of Tooling. MLSys 2022 - Diana Marculescu, Yuejie Chi, Carole-Jean Wu:
Proceedings of Machine Learning and Systems 2022, MLSys 2022, Santa Clara, CA, USA, August 29 - September 1, 2022. mlsys.org 2022 [contents] - 2021
- James Gleeson, Moshe Gabel, Gennady Pekhimenko, Eyal de Lara, Srivatsan Krishnan, Vijay Janapa Reddi:
RL-Scope: Cross-stack Profiling for Deep Reinforcement Learning Workloads. MLSys 2021 - Chi Wang, Qingyun Wu, Markus Weimer, Erkang Zhu:
FLAML: A Fast and Lightweight AutoML Library. MLSys 2021 - Shang Wang, Peiming Yang, Yuxuan Zheng, Xin Li, Gennady Pekhimenko:
Horizontally Fused Training Array: An Effective Hardware Utilization Squeezer for Training Novel Deep Learning Models. MLSys 2021 - Yichen Yang, Phitchaya Mangpo Phothilimthana, Yisu Remy Wang, Max Willsey, Sudip Roy, Jacques Pienaar:
Equality Saturation for Tensor Graph Superoptimization. MLSys 2021 - Yue Zhao, Xiyang Hu, Cheng Cheng, Cong Wang, Changlin Wan, Wen Wang, Jianing Yang, Haoping Bai, Zheng Li, Cao Xiao, Yunlong Wang, Zhi Qiao, Jimeng Sun, Leman Akoglu:
SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier Detection. MLSys 2021 - Hamzah Abdel-Aziz, Ali Shafiee, Jong Hoon Shin, Ardavan Pedram, Joseph Hassoun:
Rethinking Floating Point Overheads for Mixed Precision DNN Accelerators. MLSys 2021 - Ahmed M. Abdelmoniem, Ahmed Elzanaty, Mohamed-Slim Alouini, Marco Canini:
An Efficient Statistical-based Gradient Compression Technique for Distributed Training Systems. MLSys 2021 - Saurabh Agarwal, Hongyi Wang, Kangwook Lee, Shivaram Venkataraman, Dimitris S. Papailiopoulos:
Adaptive Gradient Communication via Critical Learning Regime Identification. MLSys 2021