@inproceedings{c749f987d5164c508d3fdb5189023aba,
title = "Artifact: Masa: Responsive Multi-DNN Inference on the Edge",
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 ",
keywords = "edge devices, mean response time, memory-aware scheduling, Multiple DNNs inference",
author = "Bart Cox and Jeroen Galjaard and Amirmasoud Ghiassi and Robert Birke and Chen, {Lydia Y.}",
year = "2021",
doi = "10.1109/PerComWorkshops51409.2021.9431004",
language = "English",
series = "2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2021",
publisher = "Institute of Electrical and Electronics Engineers (IEEE)",
pages = "446--447",
booktitle = "2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2021",
address = "United States",
note = "2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2021 ; Conference date: 22-03-2021 Through 26-03-2021",
}