FET: Small: A Portable and Intelligent Testing System for Power-efficient and Accurate Foodborne Pathogen Detection

食源性疾病是一种常见的、令人痛苦的、甚至威胁到世界各地数百万人生命的问题。食品检测正在成为我们日常生活中的一种普遍需求，即一个灵敏而经济的检测器可以保证食品的安全和质量，以满足严格的食品法规和消费者的需求。然而，目前的电化学传感器不仅需要漫长而复杂的样品处理，而且需要昂贵的设备才能达到足够的纯化和富集水平。与此同时，低成本的传感器会产生噪声信号，需要计算成本高昂的机器学习模型来进行噪声鲁棒处理。为了解决这些问题，本研究项目将开发一种便携、经济、准确的智能食品病原体检测系统。该项目的成功将使负担得起、便携和准确的食源性病原体检测成为可能，包括食品分析、食品安全、食源性疾病检测和治疗、水测量以及许多与微生物组相关的环境监测。该项目将为便携式，一次性，智能食源性病原体检测提供智力基础，扩展新兴的生物传感器，机器学习和硬件加速器设计研究社区。具体而言，该项目旨在开发(1)便携式电化学传感系统，检测受污染食品中的食源性病原体，(2)硬件友好的位机器学习模型，用于实际样品的降噪和识别，以及(3)用于位机器学习推断的超低功耗便携式加速器。这三个目标是以协作的方式提高识别性能，其中基于机器学习的信号处理支持可负担的传感技术，而硬件算法协同设计控制系统级效率。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。

Foodborne illness is a common, distressing, and even life-threatening problem for millions of people around the world. Food testing is becoming a common need for our daily lives, i.e., a sensitive and cost-effective detector can guarantee the safety and quality of food to fulfill strict food legislation and consumer demands. However, current electrochemical sensors not only require lengthy and complex sample processing, but also need expensive equipment for an adequate level of purification and enrichment. Meanwhile, low-cost sensors produce noisy signals that require computationally expensive machine-learning models for noise-robust processing. To remedy these issues, this research project is to develop an intelligent food-pathogen detection system, which is portable and affordable, yet accurate. The success of this project will enable affordable, portable and accurate food-borne pathogen detection, including food analysis, food safety, food-borne illness detection and therapy, water measurement, and many microbiome-related environmental monitoring. This project will provide an intellectual foundation for portable, disposable, intelligent foodborne pathogen detection expanding the burgeoning biosensor, machine learning and hardware accelerator design research community.Specifically, this project aims to develop (1) a portable electrochemical sensing system detecting foodborne pathogens in contaminated foods, (2) hardware-friendly bitwise machine learning models for de-noising and recognition of real-samples and (3) an ultra-low power and portable accelerator for bit-wise machine learning inferences. The three aims are to improve the recognition performance in a collaborative fashion, where affordable sensing technology is backed up by machine-learning-based signal processing, while the hardware-algorithm co-design controls system-level efficiency.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.