CAREER: Bio-Inspired Sensory Interfaces Incorporating Embedded Classification and Encryption

职业：结合嵌入式分类和加密的仿生传感接口

• 批准号: 1953801
• 负责人: Vanessa Chen
• 金额: $ 45.83万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2025-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1953801&HistoricalAwards=false
• 关键词: CAREER; Bio; Inspired; Sensory; Interfaces

Ubiquitous sensing and computing, leading to rapid growth of big data analysis, will potentially transform the world. That vision creates new challenges for pervasive sensory interfaces to enable the always-on feature, rapid analysis of information, and design for security to prevent cyberattacks. In the meantime, however, significant power will be consumed to run machine learning and complex cryptography algorithms. Critical challenges also exist in integrating classifiers and security measures into sensors to enable continuous monitoring. This project proposes an integrated program of research, education, and outreach to develop low-power sensory systems with theory, algorithms and architectures to enable in-sensor intelligence and security. The transformative aspects of this research project include fundamental understanding of bio-inspired computing, discovering useful intrinsic device characteristics, analysis of real-time data with adaptive machine learning, and exploring chaotic behaviors for efficient encryption. This research will have a significant impact on the needs of society for secure and continuous real-time monitoring to improve health, transportation, and environment through the developments of ubiquitous sensing and computing. This project also incorporates an integrated education plan to inspire and motivate younger generations with diverse backgrounds, in particular women and underrepresented minorities, to pursue education in Science, Technology, Engineering and Mathematics (STEM) fields. The plan will introduce the concepts of secure ubiquitous sensing and computing to undergraduate and graduate students, and create strong outreach activities to local K-12 students by illustrating easily-understood concepts of fundamental electronics and mathematics with compelling examples.The goal of this project is to develop ultra-low-power sensory interfaces that integrate autonomous sensing, classification, and secure measures into a single hardware platform. The bio-inspired classifiers incorporating combinatorial intrinsic characteristics emulate sophisticated biological systems where sensing, learning, and decision making are carried out through nonlinear and adaptive analog computing. The proposed architecture is driven by fast regeneration to extract relative timing information for hierarchical classification. Instead of using linear amplification and fine integration, inherent device mismatch and nonlinearity are exploited in time domain to achieve energy-efficient computation under low supply voltages. To process real-time data in sensors, Bernoulli variational distributions are employed for approximating the posterior to develop a computationally-efficient multi-layer neural network with Bayesian methods. The algorithm integrates medical knowledge and statistical analysis into the training process for adaptation to incoming signals. The proposed algorithm explores maximum sparsity in both sample and feature spaces, where regularizations of hardware constraints are included in the model to ensure robustness. Moreover, to perform encryption in sensors, the information will be randomized into deterministic noise for transmission. The pipeline chaotic system can be trained with time-varying maps to enhance the strength of the security without creating observable patterns to counter side-channel attacks. The transformation function is built with combinatorial intrinsic characteristics, which are physically unclonable to ensure complete security measures. This ensures data integrity and basic authentication for multi-layer security schemes from the edge sensors to the cloud while classification algorithms are performed locally in sensors to achieve rapid analysis and data reduction for wireless communications.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

无处不在的传感和计算，导致大数据分析的快速增长，将有可能改变世界。这一愿景为无处不在的感官接口带来了新的挑战，这些接口需要实现始终在线的功能、信息的快速分析以及防止网络攻击的安全设计。然而，与此同时，运行机器学习和复杂的加密算法将消耗大量电力。在将分类器和安全措施集成到传感器中以实现持续监控方面也存在重大挑战。该项目提出了一个综合的研究、教育和推广计划，以开发具有理论、算法和架构的低功耗传感器系统，以实现传感器内的智能和安全。该研究项目的变革方面包括对生物启发计算的基本理解，发现有用的内在设备特性，使用自适应机器学习分析实时数据，以及探索有效加密的混沌行为。这项研究将对社会对安全和持续实时监测的需求产生重大影响，通过无处不在的传感和计算的发展来改善健康、交通和环境。该项目还纳入了一项综合教育计划，以激励和激励具有不同背景的年轻一代，特别是妇女和代表性不足的少数民族，接受科学、技术、工程和数学（STEM）领域的教育。该计划将向本科生和研究生介绍安全无处不在的传感和计算概念，并通过引人注目的例子说明基础电子和数学的简单概念，为当地K-12学生开展强有力的推广活动。该项目的目标是开发超低功耗传感器接口，将自主传感、分类和安全措施集成到单个硬件平台中。结合组合内在特征的生物启发分类器模拟复杂的生物系统，其中通过非线性和自适应模拟计算进行感知，学习和决策。该体系结构由快速再生驱动，以提取相对时间信息进行分层分类。在低电源电压下，利用器件固有的失配和非线性来实现节能计算，而不是利用线性放大和精细集成。为了处理传感器中的实时数据，采用伯努利变分分布近似后验，利用贝叶斯方法建立了计算效率高的多层神经网络。该算法将医学知识和统计分析整合到训练过程中，以适应输入的信号。该算法在样本和特征空间中探索最大稀疏性，其中硬件约束的正则化包含在模型中以确保鲁棒性。此外，为了在传感器中进行加密，信息将被随机化成确定性噪声进行传输。利用时变映射对管道混沌系统进行训练，可以在不产生可观察模式的情况下增强系统的安全性，以对抗侧信道攻击。转换函数具有组合的内在特征，这些特征在物理上是不可克隆的，以确保完整的安全措施。这确保了从边缘传感器到云的多层安全方案的数据完整性和基本认证，而分类算法在传感器中本地执行，以实现无线通信的快速分析和数据缩减。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Class-E Power Amplifiers Incorporating Fingerprint Augmentation With Combinatorial Security Primitives for Machine-Learning-Based Authentication in 65 nm CMOS

E 类功率放大器将指纹增强与组合安全原语相结合，用于 65 nm CMOS 中基于机器学习的身份验证

• DOI: 10.1109/tcsi.2022.3141336
• 发表时间: 2022
• 期刊: IEEE Transactions on Circuits and Systems I: Regular Papers
• 作者: Shen, Yuyi;Xu, Jiachen;Yi, Jinho;Chen, Ethan;Chen, Vanessa
• 通讯作者: Chen, Vanessa

Wireless Bayesian Neural Networks with Self-Assembly DNA Memory and Spin-Torque Oscillators

具有自组装 DNA 存储器和自旋扭矩振荡器的无线贝叶斯神经网络

• DOI: 10.1109/mwscas48704.2020.9184674
• 发表时间: 2020
• 期刊: 2020 IEEE 63rd International Midwest Symposium on Circuits and Systems (MWSCAS
• 作者: Chen, Ethan;Xu, Jiachen;Zhu, Jian-Gang Jimmy;Chen, Vanessa
• 通讯作者: Chen, Vanessa

In-sensor time-domain classifiers using pseudo sigmoid activation functions

使用伪 sigmoid 激活函数的传感器内时域分类器

• DOI: 10.1016/j.vlsi.2020.03.002
• 发表时间: 2020
• 期刊: Integration
• 影响因子: 1.9
• 作者: Chen, Ethan;Chen, Vanessa
• 通讯作者: Chen, Vanessa

Bayesian Neural Networks for Identification and Classification of Radio Frequency Transmitters Using Power Amplifiers’ Nonlinearity Signatures

使用功率放大器的贝叶斯神经网络对射频发射器进行识别和分类 – 非线性特征

• DOI: 10.1109/ojcas.2021.3089499
• 发表时间: 2021
• 期刊: IEEE Open Journal of Circuits and Systems
• 影响因子: 2.6
• 作者: Xu, Jiachen;Shen, Yuyi;Chen, Ethan;Chen, Vanessa
• 通讯作者: Chen, Vanessa

Statistical RF/Analog Integrated Circuit Design Using Combinatorial Randomness for Hardware Security Applications

使用组合随机性进行硬件安全应用的统计射频/模拟集成电路设计

• DOI: 10.3390/math8050829
• 发表时间: 2020
• 期刊: Mathematics
• 影响因子: 2.4
• 作者: Chen, Ethan;Chen, Vanessa
• 通讯作者: Chen, Vanessa