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10 results (0.011 seconds)
1) AGGREGATHOR: Byzantine Machine Learning via Robust Gradient Aggregation
Georgios Damaskinos, El Mahdi El Mhamdi, Rachid Guerraoui, Arsany Guirguis, Sebastien Rouault
Where published: SysML'19
Article: PDF
Artifact DOI: 10.5281/zenodo.2548779
Unified artifact appendix: Link
Artifact before standardization: GitHub
Standardized CK workflow: Link (ReproIndex)
Standardized CK pipelines (programs):
Reproducible results: Open review via GitHub issues
Some results replicated:
Reproducible methodology: ACM and cTuning
2) Beyond Data and Model Parallelism for Deep Neural Networks
Zhihao Jia, Matei Zaharia, Alex Aiken
Where published: SysML'19
Article: PDF
Artifact DOI: 10.5281/zenodo.2549847
Unified artifact appendix: Link
Artifact before standardization: GitHub
Some results replicated:
Reproducible methodology: ACM and cTuning
3) Highly Efficient 8-bit Low Precision Inference of Convolutional Neural Networks with IntelCaffe
Jiong Gong, Haihao Shen, Guoming Zhang, Xiaoli Liu, Shane Li, Ge Jin, Niharika Maheshwari, Evarist Fomenko, Eden Segal
Where published: ReQuEST-ASPLOS'18
Article DOI: 10.1145/3229762.3229763
Artifact DOI: 10.1145/3229769
Unified artifact appendix: Link
Artifact before standardization: GitHub
Standardized CK workflow: Link (ReproIndex)
Standardized CK pipelines (programs):
Reproducible results: CK format
Reproducible methodology: Link
Dashboard with results: cKnowledge.org/dashboard/request.asplos18
ReproIndex JSON meta ]  [ paper ]
4) Kernel machines that adapt to GPUs for effective large batch training
Siyuan Ma, Mikhail Belkin
Where published: SysML'19
Article: PDF
Artifact DOI: 10.5281/zenodo.2574996
Unified artifact appendix: Link
Artifact before standardization: GitHub
Some results replicated:
Reproducible methodology: ACM and cTuning
5) Leveraging the VTA-TVM Hardware-Software Stack for FPGA Acceleration of 8-bit ResNet-18 Inference
Thierry Moreau, Tianqi Chen, Luis Ceze
Where published: ReQuEST-ASPLOS'18
Article DOI: 10.1145/3229762.3229766
Artifact DOI: 10.1145/3229772
Unified artifact appendix: Link
Artifact before standardization: GitHub
Standardized CK workflow: Link (ReproIndex)
Standardized CK pipelines (programs):
Reproducible results: CK format
Reproducible methodology: Link
Dashboard with results: cKnowledge.org/dashboard/request.asplos18
ReproIndex JSON meta ]  [ paper ]
6) Multi-objective autotuning of MobileNets across the full software/hardware stack
Anton Lokhmotov, Nikolay Chunosov, Flavio Vella, Grigori Fursin
Where published: ReQuEST-ASPLOS'18
Article DOI: 10.1145/3229762.3229767
Artifact DOI: 10.1145/3229773
Unified artifact appendix: Link
Artifact before standardization: GitHub
Standardized CK workflow: Link (ReproIndex)
Standardized CK pipelines (programs):
Reproducible results: CK format
Reproducible methodology: Link
Dashboard with results: cKnowledge.org/dashboard/request.asplos18
ReproIndex JSON meta ]  [ paper ]
7) Optimizing Deep Learning Workloads on ARM GPU with TVM
Lianmin Zheng, Tianqi Chen
Where published: ReQuEST-ASPLOS'18
Article DOI: 10.1145/3229762.3229764
Artifact DOI: 10.1145/3229770
Unified artifact appendix: Link
Artifact before standardization: GitHUB
Standardized CK workflow: Link (ReproIndex)
Standardized CK pipelines (programs):
Reproducible results: CK format
Reproducible methodology: Link
Dashboard with results: cKnowledge.org/dashboard/request.asplos18
ReproIndex JSON meta ]  [ paper ]
8) Optimizing DNN Computation with Relaxed Graph Substitutions
Zhihao Jia, James Thomas, Todd Warszawski, Mingyu Gao, Matei Zaharia, Alex Aiken
Where published: SysML'19
Article: PDF
Artifact DOI: 10.5281/zenodo.2549853
Unified artifact appendix: Link
Artifact before standardization: GitHub
Some results replicated:
Reproducible methodology: ACM and cTuning
9) Priority-based Parameter Propagation for Distributed DNN Training
Anand Jayarajan, Jinliang Wei, Garth Gibson, Alexandra Fedorova, Gennady Pekhimenko
Where published: SysML'19
Article: PDF
Artifact DOI: 10.5281/zenodo.2549852
Unified artifact appendix: Link
Artifact before standardization: GitHub
Standardized CK workflow: Link (ReproIndex)
Standardized CK pipelines (programs):
Reproducible results: https://github.com/ctuning/reproduce-sysml19-paper-p3/issues
Some results replicated:
Reproducible methodology: ACM and cTuning
10) Real-Time Image Recognition Using Collaborative IoT Devices
Ramyad Hadidi, Jiashen Cao, Matthew Woodward, Michael S. Ryoo, Hyesoon Kim
Where published: ReQuEST-ASPLOS'18
Article DOI: 10.1145/3229762.3229765
Artifact DOI: 10.1145/3229771
Unified artifact appendix: Link
Artifact before standardization: GitHub
Standardized CK workflow: Link (ReproIndex)
Standardized CK pipelines (programs):
Reproducible results: CK format
Reproducible methodology: Link
Dashboard with results: cKnowledge.org/dashboard/request.asplos18
ReproIndex JSON meta ]  [ paper ]