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Tensor Processing Primitives: A Programming Abstraction for Efficiency and Portability in Deep Learning & HPC Workloads
12 April 2021
E. Georganas
Dhiraj D. Kalamkar
Sasikanth Avancha
Menachem Adelman
Deepti Aggarwal
Cristina S. Anderson
Alexander Breuer
Jeremy Bruestle
Narendra Chaudhary
Abhisek Kundu
Denise Kutnick
Frank Laub
Md. Vasimuddin
Sanchit Misra
Ramanarayan Mohanty
Hans Pabst
Brian Retford
Barukh Ziv
A. Heinecke
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Papers citing
"Tensor Processing Primitives: A Programming Abstraction for Efficiency and Portability in Deep Learning & HPC Workloads"
4 / 4 papers shown
Title
DistGNN-MB: Distributed Large-Scale Graph Neural Network Training on x86 via Minibatch Sampling
Md. Vasimuddin
Ramanarayan Mohanty
Sanchit Misra
Sasikanth Avancha
GNN
13
1
0
11 Nov 2022
Next-Generation Local Time Stepping for the ADER-DG Finite Element Method
Alexander Breuer
A. Heinecke
9
6
0
21 Feb 2022
Aggregated Residual Transformations for Deep Neural Networks
Saining Xie
Ross B. Girshick
Piotr Dollár
Z. Tu
Kaiming He
268
10,196
0
16 Nov 2016
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
Yonghui Wu
M. Schuster
Z. Chen
Quoc V. Le
Mohammad Norouzi
...
Alex Rudnick
Oriol Vinyals
G. Corrado
Macduff Hughes
J. Dean
AIMat
716
6,740
0
26 Sep 2016
1