Jun 29, 2020 · In this paper, we identify and isolate the structured optimization problem at the core of device placement of DNN operators, for both inference ...
In this paper, we identify and isolate the structured optimization problem at the core of device placement of DNN operators, for both inference and training, ...
In this paper, we identify and isolate the structured optimization problem at the core of device placement of DNN operators, for both inference and training, ...
How to train DNNs efficiently? Data parallelism: • Replicate model on every worker. • Train on disjoint samples. But:.
Oct 29, 2020 · In this paper, we identify and isolate the structured optimization problem at the core of device placement of DNN operators, for both inference ...
This code package contains algorithms and input files (DNN workloads) from the paper "Efficient Algorithms for Device Placement of DNN Graph Operators"
Dec 6, 2020 · Neural Information Processing Systems (NeurIPS) is a multi-track machine learning and computational neuroscience conference that includes ...
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2020 Efficient Algorithms for Device Placement of DNN Graph Operators Jakub Tarnawski, Amar Phanishayee, Nikhil R. Devanur, Divya Mahajan, Fanny Nina Paravecino
Efficient Algorithms for Device Placement of DNN Graph Operators. 03:20. Efficient Algorithms for Device Placement of DNN Graph Operators. Watch later.
Efficient Algorithms for Device Placement of DNN Graph Operators · Jakub Tarnawski, Amar Phanishayee, Nikhil R. Devanur, Divya Mahajan, Fanny Nina Paravecino.