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Vulnerability of Hardware Neural Networks to Dynamic Operation Point Variations.

, , , , and . IEEE Des. Test, 37 (5): 75-84 (2020)

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Local Binary Pattern Networks., , , , , and . WACV, page 814-823. IEEE, (2020)Resource Efficient and Error Resilient Neural Networks.. University of California, San Diego, USA, (2019)base-search.net (ftcdlib:qt6fw3798s).An assessment of vulnerability of hardware neural networks to dynamic voltage and temperature variations., , , and . ICCAD, page 945-950. IEEE, (2017)Training Machine Learning Network on Adreno Mobile GPUs Using OpenCL., , , , , and . IWOCL, page 25:1-25:2. ACM, (2020)Multi-tenant mobile offloading systems for real-time computer vision applications., , , and . ICDCN, page 21-30. ACM, (2019)Accelerating Binarized Convolutional Neural Networks with Software-Programmable FPGAs., , , , , , , and . FPGA, page 15-24. ACM, (2017)Accelerating Local Binary Pattern Networks with Software-Programmable FPGAs., , , , and . DATE, page 1112-1117. IEEE, (2019)Local Binary Pattern Networks., , , and . CoRR, (2018)Vulnerability of Hardware Neural Networks to Dynamic Operation Point Variations., , , , and . IEEE Des. Test, 37 (5): 75-84 (2020)Binarized Convolutional Neural Networks with Separable Filters for Efficient Hardware Acceleration., , , , , , and . CVPR Workshops, page 344-352. IEEE Computer Society, (2017)