DIDL'20: Proceedings of the Workshop on Distributed Infrastructures for Deep Learning
2020 Proceeding
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MIDDLEWARE '24
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short-paper
Graph Representation Matters in Device Placement
Modern Neural Network (NN) models require more data and parameters to perform ever more complicated tasks. One approach to train a massive NN is to distribute it across multiple devices. This approach raises a problem known as the device placement ...
short-paper
Tools and Techniques for Privacy-aware, Edge-centric Distributed Deep Learning
Training and inferencing phases of Deep Learning (DL) are compute-intensive that require substantial amount of cloud-hosted resources. However, real-time needs of some edge-based applications as well as the variable and wildly fluctuating edge-cloud ...