AI hardware oriented neural network physical unclonable function and its evaluation

被引：0

作者：

Nozaki Y. ^{[1
]}

Shibagaki K. ^{[2
]}

Takemoto S. ^{[2
]}

Yoshikawa M. ^{[1
]}

机构：

[1] Faculty of Science and Technology, Meijo University, 1-501, Shiogamaguchi, Tenpaku-ku, Nagoya, Aichi

[2] Graduate School of Science and Technology, Meijo University, 1-501, Shiogamaguchi, Tenpaku-ku, Nagoya, Aichi

来源：

IEEJ Transactions on Electronics, Information and Systems | 2020年 / 140卷 / 07期

关键词：

AI hardware; Authentication; Hardware security; Neural network; Physical unclonable function;

D O I：

10.1541/ieejeiss.140.689

中图分类号：

学科分类号：

摘要：

AI techniques are required for realizing society 5.0. For the issues of societal implementation for AI, an AI hardware, which is enhanced security performances including authentication, and so on, is needed in order to reduce security risks. This study proposes a new physical unclonable function (PUF) based on neural network (NN) called NN PUF. The proposed NN PUF uses a difference of calculation time in NN due to production variations of semiconductor. Evaluation experiments using a field programmable gate array (FPGA) prove the effectiveness of the proposed NN PUF. © 2020 The Institute of Electrical Engineers of Japan.

引用

页码：689 / 696

页数：7

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