小面積・低遅延を指向したNN PUFとその評価

カテゴリ: 論文誌(論文単位)

グループ名: 【C】電子・情報・システム部門

発行日: 2020/12/01

タイトル(英語): Small Scale and Low Latency Oriented Neural Network Physical Unclonable Function and its Evaluation

著者名: 竹本　修（名城大学 大学院 理工学研究科），柴垣　和也（名城大学 大学院 理工学研究科），野崎　佑典（名城大学 理工学部），吉川　雅弥（名城大学 理工学部）

著者名(英語): Shu Takemoto (Graduate School of Science and Technology, Meijo University), Kazuya Shibagaki (Graduate School of Science and Technology, Meijo University), Yusuke Nozaki (Faculty of Science and Technology, Meijo University), Masaya Yoshikawa (Faculty of Science and Technology, Meijo University)

キーワード: ハードウェアセキュリティ，人工知能（AI），ニューラルネットワーク（NN），認証，物理的複製不可能関数（PUF）　　hardware security，artificial intelligence，neural network，authentication，physical unclonable function

要約(英語): In edge computing, edge AI that is oriented to low-latency implementation is attracting attention. Also, with the development of deep learning in recent years, the scale of neural networks implemented on edge AI has been increasing. Therefore, small scale implementation of edge AI is important. On the other hand, individual authentication of semiconductors is urgently needed due to increasing the threat of counterfeit semiconductors. For this reason, an NN PUF has been proposed that implements both Neural Network (NN) and Physical Unclonable Function (PUF) as the individual authentication function of semiconductors. The conventional NN PUF is difficult to reduce the circuit scale due to the large implementation overhead. Therefore, this study proposes a small scale and low latency oriented new NN PUF based on the conventional method. In addition, evaluation experiments using an evaluation board verify the performance for NN and PUF.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.140 No.12 （2020） 特集：電気・電子・情報関係学会東海支部連合大会

本誌掲載ページ: 1297-1306 p

原稿種別: 論文／日本語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/140/12/140_1297/_article/-char/ja/