Artifact

Masa: Responsive Multi-DNN Inference on the Edge

Conference Paper (2021)
Author(s)

B.A. Cox (TU Delft - Data-Intensive Systems)

J.M. Galjaard (Student TU Delft)

S. Ghiassi (TU Delft - Data-Intensive Systems)

Robert Birke (ABB (Switzerland))

Lydia Y. Chen (TU Delft - Data-Intensive Systems)

Research Group
Data-Intensive Systems
DOI related publication
https://doi.org/10.1109/PerComWorkshops51409.2021.9431004
More Info
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Publication Year
2021
Language
English
Research Group
Data-Intensive Systems
Pages (from-to)
446-447
ISBN (electronic)
9781665404242

Abstract

This artifact is a guideline how the Edgecaffe framework, presented in [1], can be used. Edgecaffe is an open-source Deep Neural Network framework for efficient multi-network inference on edge devices. This framework enables the layer by layer execution and fine-grained control during inference of Deep Neural Networks. Edgecaffe is created to give more fine grained-control over the execution during inference than offered by the original code of Caffe [2]. Edgecaffe made it possible for Masa to outperform Deepeye [3] and normal bulk execution. Besides the core implementation of Edgecaffe, the repository holds additional tools, Queue Runner and ModelSplitter, that make more convenient to run experiments and prepare newly trained networks

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