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: 2312157
• 负责人: Xulong Tang
• 金额: $ 34.99万
• 依托单位: University of Pittsburgh
• 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=2312157&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（DNNS）在新兴的应用领域（例如机器人辅助的老年保健，移动诊断和野生动植物监视）中广受欢迎。这些应用程序通常（i）采用连续的在线学习，使用流培训数据基于DNN模型进行微调，以服务于加班推理请求以及（ii）在能源受限的边缘设备上部署DNN模型。因此，模型适应性和设备能源效率对于用户满意度至关重要。这项研究揭示了微调的冗余，并提高了计算效率，以在边缘设备上实现实用，高效和自适应的连续在线学习。该项目的教育和外展组成部分包括（i）课程和课程项目扩展有关深度学习和边缘计算的信息。 （ii）通过PIS研究所的高级课程项目和外展计划，让本科生从事研究活动。 （iii）增加女性和少数群体在计算机科学和工程中的参与和可见性。这项研究旨在同时实现适应性和能源效率，以在边缘设备上连续在线学习。 （i）它通过自动和动态冻结融合层来降低注意力引导的智能层冻结，以降低计算成本。 （ii）它为边缘设备设计了一个有效的现场学习框架。该框架有选择地延迟并合并微调迭代，以降低微调频率并处理方案变化。 （iii）它设计了硬件支持记忆，以减少微调计算和内存访问的数量。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，通过评估被认为是珍贵的支持。