Print Email Facebook Twitter Multi-inference on the Edge: Scheduling Networks with Limited Available Memory Title Multi-inference on the Edge: Scheduling Networks with Limited Available Memory Author Galjaard, Jeroen (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor Chen, Lydia Y. (mentor) Ghiassi, S. (graduation committee) Cox, B.A. (graduation committee) Degree granting institution Delft University of Technology Programme Computer Science and Engineering Project CSE3000 Research Project Date 2020-06-25 Abstract The execution of multi-inference tasks on low-powered edge devices has become increasingly popular in recent years for adding value to data on-device. The focus of the optimization of such jobs has been on hardware, neural network architectures, and frameworks to reduce execution speed. However, it is yet not known how different scheduling policies affect the execution speed of a multi-inference job. An empirical study has been performed to investigate the effects of scheduling policies on multi-inference. The execution performance information of multi-inference batch jobs under combinations of loading and scheduling policies were determined under varying levels of constrained memory. These results were obtained using EdgeCaffe: a framework developed to execute Caffe networks on edge oriented devices. Our research showed that a novel scheduling policy, MeMa, can significantly reduce execution speed under stringent memory availability. Overall, this study demonstrates that scheduling policies can significantly reduce the execution speed of multi-inference jobs. Subject Edge ComputingSchedulingConstrained MemoryMemory AwareMulti-inferenceConvolutional Neural Networks To reference this document use: http://resolver.tudelft.nl/uuid:2fadc0f7-e381-419e-8593-92af74ead039 Part of collection Student theses Document type bachelor thesis Rights © 2020 Jeroen Galjaard Files PDF JEROEN_GALJAARD_paper.pdf 435.7 KB Close viewer /islandora/object/uuid:2fadc0f7-e381-419e-8593-92af74ead039/datastream/OBJ/view