Jun 13, 2017 · In this paper, we propose a method which learns to optimize device placement for TensorFlow computational graphs. Key to our method is the use ...
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Importantly, the decision of placing parts of the neural models on devices is often made by human experts based on simple heuristics and intuitions. In this ...
In this paper, we propose a method which learns to optimize device placement for TensorFlow computational graphs. Key to our method is the use of a sequence-to- ...
In this paper, we propose a method which learns to optimize device placement. Key to our method is the employment of a recurrent neural network to predict a set ...
A method for determining a placement for machine learning model operations across multiple hardware devices is described. The method includes receiving data ...
Jun 25, 2017 · Abstract. The past few years have witnessed a growth in size and computational requirements for training and inference with neural networks.
This project is part of CSE 5429 (Hardware Accelerators for Machine Learning) and aims to develop a Neural Machine Transalation model to map the nodes of ...
This needs to include both speed of operation and linkages between devices and adjacent operations on the graph. • Currently done by human experts who ...
Sep 12, 2024 · This paper presents a framework to tackle combinatorial optimization problems using neural networks and reinforcement learning. We focus on the ...
Device Placement Optimization with Reinforcement Learning. Azalia Mirhoseini ... Why device placement. ○. Trend toward many-device training, bigger ...