基于cnn的面积高效物理不可克隆函数的设计:权衡与优化

2020 European Conference on Circuit Theory and Design (ECCTD) Pub Date : 2020-09-01 DOI:10.1109/ECCTD49232.2020.9218316

T. Addabbo, A. Fort, Riccardo Moretti, M. Mugnaini, Hadis Takaloo, V. Vignoli

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引用次数: 2

摘要

我们讨论了一种面积高效的CMOS模拟核心单元的设计，实现了来自双神经元细胞神经网络(CNN)的PUF。该研究基于理论建模和数值模拟，提出了通过消除状态电容和仅依赖分布式寄生电容来大大降低面积消耗的电路解决方案。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Design of Area-Efficient Physical Unclonable Functions Derived From CNNs: Trade-Offs and Optimization

We discuss the design of an area-efficient CMOS analog core-cell implementing a PUF derived from a two-neurons Cellular Neural Network (CNN). The study is based on both theoretical modeling and numerical simulations, proposing circuit solutions in which the area consumption is strongly reduced by eliminating state capacitors and relying on distributed parasitic capacitances only.

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来源期刊

2020 European Conference on Circuit Theory and Design (ECCTD)

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