Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/155807
Title: EDLAB : a benchmark for edge deep learning accelerators
Authors: Kong, Hao
Huai, Shuo
Liu, Di
Zhang, Lei
Chen, Hui
Zhu, Shien
Li, Shiqing
Liu, Weichen
Rastogi, Manu
Subramaniam, Ravi
Athreya, Madhu
Lewis, M. Anthony
Keywords: Engineering::Computer science and engineering
Issue Date: 2021
Source: Kong, 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.2021.3095215
Project: I1801E0028
NTU NAP M4082282
SUG M4082087
Journal: IEEE Design and Test 
Abstract: A 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.
URI: https://hdl.handle.net/10356/155807
ISSN: 2168-2356
DOI: 10.1109/MDAT.2021.3095215
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.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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