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

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。这一愿景为普适传感接口带来了新的挑战，以实现始终在线的功能，快速分析信息，并设计安全性以防止网络攻击。然而，与此同时，运行机器学习和复杂的密码算法将消耗大量的电力。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关键挑战。该项目提出了一个研究，教育和推广的综合计划，以开发具有理论，算法和架构的低功耗传感系统，以实现传感器智能和安全。该研究项目的变革性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征，使用自适应机器学习分析实时数据，以及探索混沌行为以实现高效加密。这项研究将对社会安全和连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群体，接受科学、技术、工程和数学领域的教育。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的概念，并通过令人信服的例子说明基础电子学和数学的易于理解的概念，为当地K-12学生创造强有力的推广活动。该项目的目标是开发超低功耗传感接口，将自主传感，分类和安全措施集成到一个单一的硬件平台中。生物启发的分类器结合组合的内在特征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测、学习和决策。所提出的架构是由快速再生驱动，以提取相对时序信息的分层分类。在时域中利用固有的器件失配和非线性，而不是使用线性放大和精细积分，以实现低电源电压下的节能计算。为了处理传感器中的实时数据，采用Bernoulli变分分布来近似后验，以贝叶斯方法开发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集成到训练过程中，以适应输入信号。该算法在样本和特征空间中探索最大稀疏性，其中在模型中包括硬件约束的正则化以确保鲁棒性。此外，为了在传感器中执行加密，信息将被随机化为确定性噪声以进行传输。管道混沌系统可以用时变映射进行训练，以增强安全性，而不会产生可观察的模式来对抗侧信道攻击。转换函数是用组合的内在特征构建的，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端的多层安全方案的数据完整性和基本身份验证，同时在传感器中本地执行分类算法，以实现无线通信的快速分析和数据简化。该奖项反映了NSF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。

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.