Collaborative Research: CSR: Small: Expediting Continual Online Learning on Edge Platforms through Software-Hardware Co-designs

协作研究：企业社会责任：小型：通过软硬件协同设计加快边缘平台上的持续在线学习

• 批准号: 2312158
• 负责人: Yanzhi Wang
• 金额: $ 25万
• 依托单位: Northeastern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2312158&HistoricalAwards=false
• 关键词: Collaborative; Research; CSR; Small; Expediting

Deep neural networks (DNNs) have gained significant popularity in emerging application domains such as robot-assisted eldercare, mobile diagnosis, and wildlife surveillance. These applications commonly (i) employ continual online learning that fine-tunes the DNN model based using streaming-in training data to serve overtime inference requests and (ii) deploy the DNN models on energy-constrained edge devices. As such, both model adaptiveness and device energy efficiency are critical for user satisfaction. This research uncovers redundancy in fine-tuning and enhances the computation efficiency to achieve practical, efficient, and adaptive continual online learning on edge devices. This project's educational and outreach components include (i) curriculum and course project expansion on deep learning and edge computing. (ii) Engaging undergraduate students in research activities through senior course projects and outreach programs at PIs’ institute. (iii) Increasing the participation and visibility of female and minority students in computer science and engineering.This research aims to simultaneously achieve adaptiveness and energy efficiency for continual online learning on edge devices. (i) It develops an attention-guided smart layer freezing to reduce computation costs by automatically and dynamically freezing converged layers. (ii) It designs an efficient in-situation learning framework for edge devices. The framework selectively delays and merges fine-tuning iterations to reduce the fine-tuning frequency and handles scenario changes. (iii) It designs hardware-support memorization to reduce the amount of fine-tuning computation and memory accesses.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.

深度神经网络（dnn）在机器人辅助老年护理、移动诊断和野生动物监测等新兴应用领域获得了显著的普及。这些应用通常(i)采用持续的在线学习，使用流进的训练数据对DNN模型进行微调，以满足加班推理请求；（ii）在能量受限的边缘设备上部署DNN模型。因此，模型适应性和设备能效对用户满意度都至关重要。该研究揭示了微调中的冗余，提高了计算效率，实现了边缘设备上实用、高效、自适应的持续在线学习。该项目的教育和推广部分包括(i)深度学习和边缘计算的课程和课程项目扩展。（ii）通过pi所在学院的高级课程项目和外展项目，让本科生参与研究活动。（三）提高女性和少数族裔学生在计算机科学和工程领域的参与度和知名度。本研究旨在同时实现边缘设备上持续在线学习的适应性和能源效率。(1)开发了一种注意力引导的智能层冻结方法，通过自动动态冻结收敛层来降低计算成本。（ii）为边缘设备设计了高效的情境学习框架。框架有选择地延迟和合并微调迭代，以减少微调频率并处理场景更改。（iii）设计硬件支持内存，减少微调计算量和内存访问量。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。