Collaborative Research: SHF: Small: Towards Robust Deep Learning Computing on GPUs

合作研究：SHF：小型：在 GPU 上实现稳健的深度学习计算

• 批准号: 2114514
• 负责人: Hyeran Jeon
• 金额: $ 18.23万
• 依托单位: University of California - Merced
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2114514&HistoricalAwards=false
• 关键词: Collaborative; Research; SHF; Small; Towards

Graphics processing units (GPU) have become one of the most promising computing engines in many application domains such as scientific simulations and deep learning. With the massive parallel processing power provided by GPUs, most of the state-of-the-art server and edge systems employ GPUs as the core computing engines for deep-learning model training and inference. As the performance of deep learning models becomes one of the most important delimiters that determines market revenue of the model creators and the convenience of daily lives of model consumers, it is critical to enforce reliable and robust deep-learning computation. This project aims to explore the challenges and opportunities to address the reliability and privacy implications of GPU computing as a deep-learning accelerator and design lightweight protection schemes.The technical aims of this project are divided into three thrusts. The first thrust explores and evaluates possible vulnerabilities and their impact on GPU-based deep-learning computing. The second thrust tackles the vulnerabilities at the compute-unit level by redesigning GPU building blocks, such as new scheduling algorithms and activation acceleration logic. The third thrust explores selective integrity protection mechanisms in communication channels and memory subsystems to transfer data between the CPU and GPU without imposing significant performance overhead. The proposed solutions will mitigate architectural and system vulnerabilities in GPU-based deep learning computing, which will enable the deep learning algorithm developers to focus more on performance improvement and technological advancement, and the consumers to use deep learning-based cognitive products without privacy concerns. The findings of this research will be integrated into undergraduate and graduate courses as well as various outreach activities on K-12 education, and publicly shared through open-source repositories.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.

图形处理单元（GPU）已成为许多应用领域中最有前途的计算引擎之一，例如科学模拟和深度学习。借助GPU提供的庞大并行处理能力，大多数最先进的服务器和边缘系统都采用GPU作为核心计算引擎进行深度学习模型培训和推理。随着深度学习模型的性能成为决定模型创建者市场收入以及模型消费者日常生活的便利性的最重要的分界符之一，执行可靠且可靠的深度学习计算至关重要。该项目旨在探讨解决GPU计算作为深度学习加速器和设计轻量级保护方案的可靠性和隐私影响的挑战和机会。该项目的技术目标分为三个推力。第一个推力探索并评估了可能的漏洞及其对基于GPU的深度学习计算的影响。第二个推力通过重新设计GPU构建块（例如新的调度算法和激活加速逻辑）来解决计算单元级别的漏洞。第三个推力探讨了通信渠道和内存子系统中的选择性完整性保护机制，以在CPU和GPU之间传输数据，而无需施加明显的性能开销。所提出的解决方案将减轻基于GPU的深度学习计算中的建筑和系统脆弱性，这将使深度学习算法开发人员更多地专注于绩效改进和技术进步，而消费者可以使用无隐私问题的深度学习认知产品。这项研究的结果将纳入本科和研究生课程以及有关K-12教育的各种外展活动，并通过开源存储库公开共享。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的评估审查审查标准来通过评估来通过评估来获得支持的。