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CAREER: An Ultra-low Power Analog Computing Hardware Design Framework for Machine Learning Inference in Edge Biomedical Devices

职业：用于边缘生物医学设备中机器学习推理的超低功耗模拟计算硬件设计框架

基本信息

批准号：
2144703
负责人：
Aatmesh Shrivastava
金额：
$ 49.99万
依托单位：
Northeastern University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-04-15 至 2027-03-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2144703&HistoricalAwards=false
关键词：
CAREER Ultra low Power Analog

项目摘要

Wearable and implantable biomedical devices have advanced healthcare through the development of continuous sensing, monitoring, and timely medical interventions. Their growth has been enabled by innovation in technologies and low-power system design. We are also seeing a rapid adoption of machine-learning (ML) techniques in healthcare applications. However, a significant technological gap exists when it comes to adopting ML techniques for wearable and implantable biomedical hardware because of the relatively high power consumption and chip area requirements associated with ML solutions. This project aims to develop ML-based hardware solutions for wearable and implantable biomedical devices. To realize power and device size goals, analog computing will be used to develop the ML hardware platform. The proposed analog computing platform will be used to develop ultra-low power ML hardware for detecting arrythmia and obstructive sleep apnea. The research outcomes from this project will be integrated into education to develop new graduate and undergraduate courses on ML and power management. Undergraduate and high-school students will participate in the research through the Young Scholar Program (YSP) and REU outreach activities. The project will provide a platform for training graduate and undergraduate students on biomedical devices, circuit and chip design, and ML hardware design. Outreach activities will involve K-12 students.This project is centered around the realization of ultra-low power (ULP) machine learning (ML) system-on-chip (SoC) hardware with inference capability for wearable and implantable biomedical applications. Power consumption and device size requirements make it challenging to integrate ML solutions in mobile health devices. To realize the power consumption and device size minimization goals, robust sub-threshold analog computing circuits will be developed while overcoming variability issues previously associated with analog computing. A new analog system modeling and simulation tool will be created to associate power consumption, noise, linearity, and other performance goals of analog circuits with the classification accuracy of a given ML network to realize area, power, and performance optimized ML hardware. The analog computing hardware framework will be developed with a new constant transconductance-based sub-threshold design to realize high energy efficiency and robustness goals. The approach will also support multi-layer analog computing designs without the need for interfacing amplifiers or converters for signal conditioning. The resulting modeling and simulation tool will associate circuit design goals with ML classification accuracy. It will further help in reducing power and area to develop tailored analog circuits for ML networks with specific power and performance goals while maintaining the required classification accuracies. To exemplify the design approach, the analog system modeling tool and robust analog computing circuits will be utilized to develop an ultra-low power analog SoC to demonstrate ML applications for ECG and pulse oximetry, with the goal to show up to 50-times reduction in power consumption while maintaining a high classification accuracy.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.

可穿戴和植入式生物医学设备通过开发连续感测、监测和及时的医疗干预来推进医疗保健。它们的增长得益于技术创新和低功耗系统设计。我们还看到机器学习（ML）技术在医疗保健应用中的快速采用。然而，在将ML技术用于可穿戴和可植入生物医学硬件方面存在重大技术差距，因为与ML解决方案相关的功耗和芯片面积要求相对较高。该项目旨在为可穿戴和植入式生物医学设备开发基于ML的硬件解决方案。为了实现功率和器件尺寸目标，模拟计算将用于开发ML硬件平台。拟议的模拟计算平台将用于开发超低功耗ML硬件，用于检测心律失常和阻塞性睡眠呼吸暂停。该项目的研究成果将被整合到教育中，以开发关于ML和电源管理的新的研究生和本科生课程。本科生和高中生将通过青年学者计划（YSP）和REU外展活动参与研究。该项目将为生物医学设备、电路和芯片设计以及ML硬件设计方面的研究生和本科生提供培训平台。该项目的核心是实现具有推理能力的超低功耗（ULP）机器学习（ML）片上系统（SoC）硬件，用于可穿戴和植入式生物医学应用。功耗和设备尺寸要求使得将ML解决方案集成到移动的医疗设备中具有挑战性。为了实现功耗和器件尺寸最小化的目标，将开发鲁棒的亚阈值模拟计算电路，同时克服先前与模拟计算相关的可变性问题。将创建一个新的模拟系统建模和仿真工具，将模拟电路的功耗、噪声、线性度和其他性能目标与给定ML网络的分类准确性关联起来，以实现面积、功耗和性能优化的ML硬件。模拟计算硬件框架将采用新的基于恒定跨导的亚阈值设计来开发，以实现高能效和鲁棒性目标。该方法还将支持多层模拟计算设计，而无需接口放大器或转换器进行信号调理。由此产生的建模和仿真工具将把电路设计目标与ML分类精度相关联。它将进一步帮助降低功耗和面积，为具有特定功耗和性能目标的ML网络开发定制的模拟电路，同时保持所需的分类精度。为了验证设计方法，模拟系统建模工具和强大的模拟计算电路将用于开发超低功耗模拟SoC，以演示用于ECG和脉搏血氧仪的ML应用，目标是达到50岁该奖项反映了NSF的法定使命，并通过使用基金会的学术价值和更广泛的影响审查标准。