SHF: Small: Novel SW/HW Approximate Computing Methodologies with Case Studies on Biometric Security Systems

Brown University, Providence RI

Investigators

Abstract

Biometric systems are increasingly deployed to meet the security demand of mobile, embedded and internet-of-things devices. The goal of this project is to devise techniques that lead to biometric systems with faster response time, less energy consumption and cost without compromising the accuracy and security. In addition to the main research goal, this project includes a comprehensive education and outreach plan that includes research experience for undergraduate students, interdisciplinary educational courses, outreach to high-school students, and tech transfer to industry. To achieve the goal of this project, new approximate computing techniques will be developed to simplify the hardware of biometric systems with little or no compromise to accuracy. The project investigates new methods for approximate logic circuit synthesis using Boolean matrix factorization, which leads to smooth trade-off between circuit complexity and quality of results. The project also investigates the use of reinforcement-learning techniques to navigate the space of potential approximate designs to identify the best ones. Finally, the proposal investigates the impact of using the proposed methods on case studies of end-to-end biometric systems, including fingerprinting, iris scanning, and 3D facial recognition. 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.