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dc.contributor.authorMazumder, Arnab
dc.contributor.authorMohsenin, Tinoosh
dcterms.creatorhttps://orcid.org/0000-0002-9550-7917en_US
dc.date.accessioned2022-05-11T13:22:09Z
dc.date.available2022-05-11T13:22:09Z
dc.date.issued2022-02-04
dc.descriptiontinyML Research Symposium 2022 Burlingame, CA March 28, 2022en_US
dc.description.abstractKeyword Spotting nowadays is an integral part of speech-oriented user interaction targeted for smart devices. To this extent, neural networks are extensively used for their flexibility and high accuracy. However, coming up with a suitable configuration for both accuracy requirements and hardware deployment is a challenge. We propose a regression-based network exploration technique that considers the scaling of the network filters (s) and quantization (q) of the network layers, leading to a friendly and energy-efficient configuration for FPGA hardware implementation. We experiment with different combinations of NN⟨q,s⟩ on the FPGA to profile the energy consumption of the deployed network so that the user can choose the most energy-efficient network configuration promptly. Our accelerator design is deployed on the Xilinx AC 701 platform and has at least 2.1× and 4× improvements on energy and energy efficiency results, respectively, compared to recent hardware implementations for keyword spotting.en_US
dc.description.urihttps://arxiv.org/abs/2202.02361en_US
dc.format.extent6 pagesen_US
dc.genrepreprintsen_US
dc.genreconference papers and proceedingsen_US
dc.identifierdoi:10.13016/m2e7sj-xwuo
dc.identifier.urihttps://doi.org/10.48550/arXiv.2202.02361
dc.identifier.urihttp://hdl.handle.net/11603/24687
dc.language.isoen_USen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Student Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.en_US
dc.titleA Fast Network Exploration Strategy to Profile Low Energy Consumption for Keyword Spottingen_US
dc.typeTexten_US


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