Award Abstract # 1953801
CAREER: Bio-Inspired Sensory Interfaces Incorporating Embedded Classification and Encryption

NSF Org: ECCS
Division of Electrical, Communications and Cyber Systems
Recipient: CARNEGIE MELLON UNIVERSITY
Initial Amendment Date: December 6, 2019
Latest Amendment Date: July 13, 2024
Award Number: 1953801
Award Instrument: Continuing Grant
Program Manager: Jenshan Lin
jenlin@nsf.gov
 (703)292-7360
ECCS
 Division of Electrical, Communications and Cyber Systems
ENG
 Directorate for Engineering
Start Date: September 1, 2019
End Date: September 30, 2026 (Estimated)
Total Intended Award Amount: $458,293.00
Total Awarded Amount to Date: $815,969.00
Funds Obligated to Date: FY 2019 = $351,519.00
FY 2021 = $106,774.00

FY 2024 = $357,676.00
History of Investigator:
  • Vanessa Chen (Principal Investigator)
    vanessachen@cmu.edu
Recipient Sponsored Research Office: Carnegie-Mellon University
5000 FORBES AVE
PITTSBURGH
PA  US  15213-3890
(412)268-8746
Sponsor Congressional District: 12
Primary Place of Performance: Carnegie-Mellon University
PA  US  15213-3815
Primary Place of Performance
Congressional District:
12
Unique Entity Identifier (UEI): U3NKNFLNQ613
Parent UEI: U3NKNFLNQ613
NSF Program(s): Eddie Bernice Johnson INCLUDES,
CCSS-Comms Circuits & Sens Sys
Primary Program Source: 01002324RB NSF RESEARCH & RELATED ACTIVIT
04AC2324DB EDU DRSA DEFC AAB

01001920DB NSF RESEARCH & RELATED ACTIVIT

01002122DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 019Z, 1045, 106E, 1504, 154E, 170E
Program Element Code(s): 032Y00, 756400
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.041, 47.076

ABSTRACT

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.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

Note:  When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

(Showing: 1 - 10 of 14)
Chen, Ethan and Chen, Vanessa "In-sensor time-domain classifiers using pseudo sigmoid activation functions" Integration , 2020 10.1016/j.vlsi.2020.03.002 Citation Details
Chen, Ethan and Chen, Vanessa "Statistical RF/Analog Integrated Circuit Design Using Combinatorial Randomness for Hardware Security Applications" Mathematics , v.8 , 2020 10.3390/math8050829 Citation Details
Chen, Ethan and Kan, John and Yang, Bo-Yuan and Zhu, Jimmy and Chen, Vanessa "Intelligent Electromagnetic Sensors for Non-Invasive Trojan Detection" Sensors , v.21 , 2021 https://doi.org/10.3390/s21248288 Citation Details
Chen, Ethan and Xu, Jiachen and Zhu, Jian-Gang Jimmy and Chen, Vanessa "Wireless Bayesian Neural Networks with Self-Assembly DNA Memory and Spin-Torque Oscillators" 2020 IEEE 63rd International Midwest Symposium on Circuits and Systems (MWSCAS) , 2020 10.1109/MWSCAS48704.2020.9184674 Citation Details
Chen, Vanessa and Xu, Jiachen and Shen, Yuyi and Chen, Ethan "RF Fingerprint Classification With Combinatorial-Randomness-Based Power Amplifiers and Convolutional Neural Networks: Secure analog/RF electronics and electromagnetics" IEEE solidstate circuits magazine , v.14 , 2022 https://doi.org/10.1109/MSSC.2022.3200302 Citation Details
Fan, Chengyu and Deng, Junting and Chen, Ethan and Chen, Vanessa "Enhancing RF Fingerprint Generation in Power Amplifiers: Unequally Spaced Multitone Design Approaches and Considerations" IEEE Open Journal of the Solid-State Circuits Society , v.4 , 2024 https://doi.org/10.1109/OJSSCS.2024.3451401 Citation Details
Hsueh, Jen-Chieh and Chen, Vanessa H.-C. "An ultra-low voltage chaos-based true random number generator for IoT applications" Microelectronics Journal , v.87 , 2019 10.1016/j.mejo.2019.03.013 Citation Details
Kan, John and Shen, Yuyi and Xu, Jiachen and Chen, Ethan and Zhu, Jimmy and Chen, Vanessa "RF Analog Hardware Trojan Detection Through Electromagnetic Side-Channel" IEEE Open Journal of Circuits and Systems , v.3 , 2022 https://doi.org/10.1109/OJCAS.2022.3210163 Citation Details
Shen, Yuyi and Xu, Jiachen and Yi, Jinho and Chen, Ethan and Chen, Vanessa "Class-E Power Amplifiers Incorporating Fingerprint Augmentation With Combinatorial Security Primitives for Machine-Learning-Based Authentication in 65 nm CMOS" IEEE Transactions on Circuits and Systems I: Regular Papers , 2022 https://doi.org/10.1109/TCSI.2022.3141336 Citation Details
Xu, Jiachen and Chen, Ethan and Chen, Vanessa "Energy-Efficient Data Symbol Detection via Boosted Learning for Multi-Actuator Data Storage Systems" 2021 IEEE International Symposium on Circuits and Systems (ISCAS) , 2021 https://doi.org/10.1109/ISCAS51556.2021.9401676 Citation Details
Xu, Jiachen and Shen, Yuyi and Chen, Ethan and Chen, Vanessa "Bayesian Neural Networks for Identification and Classification of Radio Frequency Transmitters Using Power Amplifiers Nonlinearity Signatures" IEEE Open Journal of Circuits and Systems , v.2 , 2021 https://doi.org/10.1109/OJCAS.2021.3089499 Citation Details
(Showing: 1 - 10 of 14)

Please report errors in award information by writing to: awardsearch@nsf.gov.

Print this page

Back to Top of page