Fetching the paper…
Reading the bibliography…
State-of-the-art machine learning frameworks support a wide variety of design features to enable a flexible machine learning programming interface and to ease the programmability burden on machine learning developers.
Introduction to Intel Advanced Vector Extensions
Chris Lomont. 2011 · 2011
Earlier work this paper cites.
Caffe: Convolutional architecture for fast feature embedding. In Proceedings of the 22nd ACM international conference on Multimedia . ACM, 675–678
Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama, and Trevor Darrell. 2014 · 2014
Earlier work this paper cites.
Ahmad Yasin. 2014 · 2014
Earlier work this paper cites.
MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems
Tianqi Chen, Mu Li, Yutian Li, Min Lin, Naiyan Wang, Minjie Wang, Tianjun Xiao, Bing Xu, Chiyuan Zhang, and Zheng Zhang. 2015 · 2015
Earlier work this paper cites.
Going Deeper with Convolutions. In Proceedings of the IEEE conference on Computer Vision and Pattern Recognition . 1–9
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. 2015 · 2015
Earlier work this paper cites.
TensorFlow: A System for Large-Scale Machine Learning. In OSDI , Vol. 16. 265–283
Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al · 2016
Earlier work this paper cites.
Comparative Study of Caffe, Neon, Theano, and Torch for Deep Learning
Soheil Bahrampour, Naveen Ramakrishnan, Lukas Schott, and Mohak Shah. 2016 · 2016
Earlier work this paper cites.
Wide and Deep Learning: Better Together with TensorFlow
Heng-Tze Cheng. [n.d.] · 2016
Earlier work this paper cites.
Deep Neural Networks for YouTube Recommendations. In Proceedings of the 10th ACM conference on recommender systems . ACM, 191–198
Paul Covington, Jay Adams, and Emre Sargin. 2016 · 2016
Earlier work this paper cites.
Deep Residual Learning for Image Recognition. In Proceedings of the IEEE conference on Computer Vision and Pattern Recognition . 770–778
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016 · 2016
Earlier work this paper cites.
SqueezeNet: AlexNet-Level Accuracy with 50x Fewer Parameters and <0.5 MB Model Size
Forrest N Iandola, Song Han, Matthew W Moskewicz, Khalid Ashraf, William J Dally, and Kurt Keutzer. 2016 · 2016
Earlier work this paper cites.
Benchmarking state-of-the-art deep learning software tools. In 2016 7th International Conference on Cloud Computing and Big Data (CCBD) . IEEE, 99–104
Shaohuai Shi, Qiang Wang, Pengfei Xu, and Xiaowen Chu. 2016 · 2016
Earlier work this paper cites.
Rethinking the Inception Architecture for Computer Vision. In Proceedings of the IEEE conference on Computer Vision and Pattern Recognition . 2818–2826
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. 2016 · 2016
Earlier work this paper cites.
Julia: A Fresh Approach to Numerical Computing
Jeff Bezanson, Alan Edelman, Stefan Karpinski, and Viral B Shah. 2017 · 2017
Cited alongside, same era.
Neural Collaborative Filtering. In Proceedings of the 26th international conference on world wide web . International World Wide Web Conferences Steering Committee, 173–182
Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017 · 2017
Cited alongside, same era.
Densely Connected Convolutional Networks. In Proceedings of the IEEE conference on Computer Vision and Pattern Recognition . 4700–4708
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger. 2017 · 2017
Cited alongside, same era.
Introduction to PyTorch
Nikhil Ketkar. 2017 · 2017
Cited alongside, same era.
Attention Is All You Need. In Advances in Neural Information Processing Systems . 5998–6008
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Cited alongside, same era.
Folly: Facebook Open-source Library
2019 · 2019
Closest in time.
Introducing GPipe, an Open Source Library for Efficiently Training Large-scale Neural Network Models
Google AI Blog. 2019 · 2019
Closest in time.
Eigen Thread Pool
Eigen. 2019 · 2019
Closest in time.
TensorFlow
Google. 2019 · 2019
Closest in time.
The Architectural Implications of Facebook’s DNN-based Personalized Recommendation
Udit Gupta, Xiaodong Wang, Maxim Naumov, Carole-Jean Wu, Brandon Reagen, David Brooks, Bradford Cottel, Kim Hazelwood, Bill Jia, Hsien-Hsin S Lee, Andrey Malevich, Dheevatsa Mudigere, Mikhail Smelyanskiy, Liang Xiong, and Xuan Zhang. 2019 · 2019
Closest in time.
Performance characterization of dnn training using tensorflow and pytorch on modern clusters. In 2019 IEEE International Conference on Cluster Computing (CLUSTER) . IEEE, 1–11
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Aggregated Residual Transformations for Deep Neural Networks. In Proceedings of the IEEE conference on Computer Vision and Pattern Recognition . 1492–1500
Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He. 2017 · 2017
Cited alongside, same era.
Tips to Improve Performance for Popular Deep Learning Frameworks on CPUs
P Anju. 2018 · 2018
Cited alongside, same era.
{ \{ TVM } \} : An Automated end-to-end optimizing compiler for deep learning. In 13th { \{ USENIX } \} Symposium on Operating Systems Design and Implementation ( { \{ OSDI } \} 18) . 578–594
Tianqi Chen, Thierry Moreau, Ziheng Jiang, Lianmin Zheng, Eddie Yan, Haichen Shen, Meghan Cowan, Leyuan Wang, Yuwei Hu, Luis Ceze, et al · 2018
Cited alongside, same era.
Bandana: Using Non-Volatile Memory for Storing Deep Learning Models
Assaf Eisenman, Maxim Naumov, Darryl Gardner, Misha Smelyanskiy, Sergey Pupyrev, Kim Hazelwood, Asaf Cidon, and Sachin Katti. 2018 · 2018
Cited alongside, same era.
Auto-Tuning TensorFlow Threading Model for CPU Backend
Niranjan Hasabnis. 2018 · 2018
Cited alongside, same era.
Applied Machine Learning at Facebook: A Datacenter Infrastructure Perspective. In High Performance Computer Architecture (HPCA), 2018 IEEE International Symposium on . IEEE, 620–629
Kim Hazelwood, Sarah Bird, David Brooks, Soumith Chintala, Utku Diril, Dmytro Dzhulgakov, Mohamed Fawzy, Bill Jia, Yangqing Jia, Aditya Kalro, et al · 2018
Cited alongside, same era.
Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions
Nicolas Vasilache, Oleksandr Zinenko, Theodoros Theodoridis, Priya Goyal, Zachary DeVito, William S Moses, Sven Verdoolaege, Andrew Adams, and Albert Cohen. 2018 · 2018
Cited alongside, same era.
Arpan Jain, Ammar Ahmad Awan, Quentin Anthony, Hari Subramoni, and Dhableswar K DK Panda. 2019 · 2019
Closest in time.
GeekBench v4
Primate Labs. 2019 · 2019
Closest in time.
Deep Learning Recommendation Model for Personalization and Recommendation Systems
Maxim Naumov, Dheevatsa Mudigere, Hao-Jun Michael Shi, Jianyu Huang, Narayanan Sundaraman, Jongsoo Park, Xiaodong Wang, Udit Gupta, Carole-Jean Wu, Alisson G Azzolini, Dmytro Dzhulgakov, Andrey Mallevich, Ilia Cherniavskii, Yinghai Lu, Raghuraman Krishnamoorthi, Ansha Yu, Volodymyr Kondratenko, Stephanie Pereira, Xianjie Chen, Wenlin Chen, Vijay Rao, Bill Jia, Liang Xiong, and Misha Smelyanskiy. 2019 · 2019
Closest in time.
Vijay Janapa Reddi, Christine Cheng, David Kanter, Peter Mattson, Guenther Schmuelling, Carole-Jean Wu, Brian Anderson, Maximilien Breughe, Mark Charlebois, William Chou, et al · 2019
Closest in time.
SoftSKU: Optimizing Server Architectures for Microservice Diversity at Scale. In Proceedings of the 46th International Symposium on Computer Architecture . ACM, 513–526
Akshitha Sriraman, Abhishek Dhanotia, and Thomas F Wenisch. 2019 · 2019
Closest in time.
Machine Learning at Facebook: Understanding Inference at the Edge. In 2019 IEEE International Symposium on High Performance Computer Architecture (HPCA) . IEEE, 331–344
Carole-Jean Wu, David Brooks, Kevin Chen, Douglas Chen, Sy Choudhury, Marat Dukhan, Kim Hazelwood, Eldad Isaac, Yangqing Jia, Bill Jia, et al · 2019
Closest in time.
MLPerf: An Industry Standard Benchmark Suite for Machine Learning Performance
P. Mattson, V. J. Reddi, C. Cheng, C. Coleman, G. Diamos, D. Kanter, P. Micikevicius, D. Patterson, G. Schmuelling, H. Tang, G. Wei, and Carole-Jean Wu. 2020 · 2020
Closest in time.