SBIR Phase I: A Hardware-Aware AutoML Platform for Resource-Constrained Devices

SBIR 第一阶段：适用于资源受限设备的硬件感知 AutoML 平台

• 批准号: 2136679
• 负责人: Alfredo Costilla Reyes
• 金额: $ 25.59万
• 依托单位: AI POW LLC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-15 至 2023-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2136679&HistoricalAwards=false
• 关键词: SBIR; Phase; Hardware; Aware; AutoML

The broader impact of this Small Business Innovation Research (SBIR) Phase I project will be to automate manufacturing inspection, reducing the cost of certain highly engineered projects. This project will develop a system to automatically create machine learning models for resource-constrained industrial hardware. This project enables the fast, affordable, and easy creation of on-device artificial intelligence with significant industrial applications. This will improve robust vision-inspection tools to improve product quality. This project will develop an efficient solution to create advanced industrial internet of things applications that reduce network stress, minimize latency, and increase security at the edge. While the internet of things has many applications in the manufacturing industry, the vision inspection market can benefit due to its low-latency needs and the high stress it produces in the supporting infrastructure. This project is a flexible and modular hardware-aware machine learning model generation system that reduces manual efforts by automatically generating complex machine learning models for resource-constrained devices. Technical hurdles include the generation of neural networks that consider hardware speed and capacity constraints without human intervention. Technical milestones involve creating an automated model discovery platform tailored for edge devices, new resource-constrained neural architecture search algorithms, and hardware-aware model compression. This research aims to produce a prototype that domain experts with limited computer science training can use to create advanced industrial internet of things solutions.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.

这个小型企业创新研究(SBIR)第一阶段项目的更广泛影响将是自动化制造检查，降低某些高度工程化项目的成本。这个项目将开发一个系统，为资源有限的工业硬件自动创建机器学习模型。该项目能够快速、经济、轻松地创建具有重要工业应用的设备上人工智能。这将改进强大的视觉检查工具，以提高产品质量。该项目将开发一种高效的解决方案，以创建先进的工业物联网应用，以减轻网络压力、最大限度地减少延迟并提高边缘安全性。虽然物联网在制造业有许多应用，但视觉检测市场可以受益，因为它的低延迟需求和它在支持基础设施中产生的高压力。该项目是一个灵活且模块化的硬件感知的机器学习模型生成系统，通过为资源受限的设备自动生成复杂的机器学习模型来减少手动工作。技术障碍包括在没有人工干预的情况下生成考虑硬件速度和容量限制的神经网络。技术里程碑包括创建为边缘设备量身定做的自动模型发现平台、新的资源受限神经架构搜索算法和硬件感知模型压缩。这项研究旨在产生一个原型，只接受过有限计算机科学培训的领域专家可以用来创建先进的工业物联网解决方案。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。