Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/155807
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dc.contributor.authorKong, Haoen_US
dc.contributor.authorHuai, Shuoen_US
dc.contributor.authorLiu, Dien_US
dc.contributor.authorZhang, Leien_US
dc.contributor.authorChen, Huien_US
dc.contributor.authorZhu, Shienen_US
dc.contributor.authorLi, Shiqingen_US
dc.contributor.authorLiu, Weichenen_US
dc.contributor.authorRastogi, Manuen_US
dc.contributor.authorSubramaniam, Ravien_US
dc.contributor.authorAthreya, Madhuen_US
dc.contributor.authorLewis, M. Anthonyen_US
dc.date.accessioned2022-03-22T02:21:08Z-
dc.date.available2022-03-22T02:21:08Z-
dc.date.issued2021-
dc.identifier.citationKong, H., Huai, S., Liu, D., Zhang, L., Chen, H., Zhu, S., Li, S., Liu, W., Rastogi, M., Subramaniam, R., Athreya, M. & Lewis, M. A. (2021). EDLAB : a benchmark for edge deep learning accelerators. IEEE Design and Test. https://dx.doi.org/10.1109/MDAT.2021.3095215en_US
dc.identifier.issn2168-2356en_US
dc.identifier.urihttps://hdl.handle.net/10356/155807-
dc.description.abstractA new trend tends to deploy deep learning algorithms to edge environments to mitigate privacy and latency issues from cloud computing. Diverse edge deep learning accelerators are devised to speed up the inference of deep learning algorithms on edge devices. Various edge deep learning accelerators feature different characteristics in terms of power and performance, which make it a very challenging task to efficiently and uniformly compare different accelerators. In this paper, we introduce EDLAB, an end-to-end benchmark, to evaluate the overall performance of edge deep learning accelerators. EDLAB consists of state-of-the-art deep learning models, a unified workload preprocessing and deployment framework, as well as a collection of comprehensive metrics. In addition, we propose parameterized models to model the hardware performance bound so that EDLAB can identify the hardware potentials and the hardware utilization of different deep learning applications. Finally, we employ EDLAB to benchmark three edge deep learning accelerators and analyze the benchmarking results. From the analysis we obtain some insightful observations that can guide the design of efficient deep learning applications.en_US
dc.description.sponsorshipNanyang Technological Universityen_US
dc.description.sponsorshipNational Research Foundation (NRF)en_US
dc.language.isoenen_US
dc.relationI1801E0028en_US
dc.relationNTU NAP M4082282en_US
dc.relationSUG M4082087en_US
dc.relation.ispartofIEEE Design and Testen_US
dc.rights© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/MDAT.2021.3095215.en_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleEDLAB : a benchmark for edge deep learning acceleratorsen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.contributor.researchHP-NTU Digital Manufacturing Corporate Laben_US
dc.identifier.doi10.1109/MDAT.2021.3095215-
dc.description.versionSubmitted/Accepted versionen_US
dc.subject.keywordsBenchmarken_US
dc.subject.keywordsDeep Learningen_US
dc.subject.keywordsEdge Acceleratoren_US
dc.subject.keywordsDeploymenten_US
dc.description.acknowledgementThis research was conducted in collaboration with HP Inc. and supported by National Research Foundation (NRF) Singapore and the Singapore Government through the Industry Alignment Fund-Industry Collaboration Projects Grant (I1801E0028). This work is also partially supported by NTU NAP M4082282 and SUG M4082087, Singapore.en_US
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