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

SHF：小型：新颖的软件/硬件近似计算方法以及生物识别安全系统的案例研究

• 批准号: 1814920
• 负责人: Sherief Reda
• 金额: $ 31.35万
• 依托单位: Brown University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-01 至 2023-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1814920&HistoricalAwards=false
• 关键词: SHF; Small; Novel; SW; HW

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.

生物识别系统越来越多地被部署来满足移动、嵌入式和物联网设备的安全需求。这个项目的目标是设计出响应时间更快、能耗更低、成本更低的生物识别系统，而不会影响准确性和安全性。除了主要的研究目标外，该项目还包括一个全面的教育和推广计划，其中包括面向本科生的研究经验、跨学科教育课程、面向高中生的推广以及向行业转移技术。为了实现这个项目的目标，将开发新的近似计算技术来简化生物识别系统的硬件，而不会对精度造成什么影响。该项目研究了使用布尔矩阵分解进行近似逻辑电路综合的新方法，从而在电路复杂性和结果质量之间进行了平滑的权衡。该项目还调查了强化学习技术的使用，以在潜在的近似设计空间中导航，以确定最佳设计。最后，该提案调查了使用所建议的方法对端到端生物识别系统的案例研究的影响，包括指纹、虹膜扫描和3D面部识别。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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

Approximate Logic Synthesis Using Boolean Matrix Factorization

• DOI: 10.1109/tcad.2021.3054603
• 发表时间: 2021-01
• 期刊: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
• 影响因子: 2.9
• 作者: Jingxiao Ma;S. Hashemi;S. Reda

Generalized Matrix Factorization Techniques for Approximate Logic Synthesis

近似逻辑综合的广义矩阵分解技术

• DOI: 10.23919/date.2019.8715274
• 发表时间: 2019
• 期刊: Test in Europe
• 作者: Hashemi, Soheil;Reda, Sherief
• 通讯作者: Reda, Sherief

Approximate Logic Synthesis Using BLASYS,

使用 BLASYS 进行近似逻辑综合，

• 发表时间: 2019
• 期刊: Workshop on Open-Source EDA Technology
• 作者: J. Ma, S. Hashemi

A Resource-Efficient Embedded Iris Recognition System Using Fully Convolutional Networks

使用全卷积网络的资源高效型嵌入式虹膜识别系统

• DOI: 10.1145/3357796
• 发表时间: 2020
• 期刊: ACM Journal on Emerging Technologies in Computing Systems
• 影响因子: 2.2
• 作者: Tann, Hokchhay;Zhao, Heng;Reda, Sherief
• 通讯作者: Reda, Sherief

Approximate Logic Synthesis: A Survey

• DOI: 10.1109/jproc.2020.3014430
• 发表时间: 2020-12-01
• 期刊: PROCEEDINGS OF THE IEEE
• 影响因子: 20.6
• 作者: Scarabottolo, Ilaria;Ansaloni, Giovanni;Reda, Sherief
• 通讯作者: Reda, Sherief