SHF: Small: Collaborative Research: Retraining-free Concurrent Test and Diagnosis in Emerging Neural Network Accelerators

SHF：小型：协作研究：新兴神经网络加速器中的免再训练并发测试和诊断

• 批准号: 1910022
• 负责人: Wujie Wen
• 金额: $ 23.5万
• 依托单位: Florida International University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2020-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1910022&HistoricalAwards=false
• 关键词: SHF; Small; Collaborative; Research; Retraining

Neural networks have become the go-to tool for solving many real-world recognition and classification problems in computer vision, language processing, life sciences and finance. While promising, smart and intelligent data interpretation via deep learning is extremely power hungry. To conduct power-efficient deep learning on battery-constrained edge platforms, one promising solution is to use hardware accelerators built with emerging non-volatile memory (NVM) devices, which offer high density, extremely low power consumption, as well as in-situ and parallelized data processing. While these advances are enticing, NVM devices also impose extra challenges, as their design and manufacturing technology are far less mature than CMOS. Furthermore, NVM technologies are likely to exhibit new types of errors, such as read/write disturbance, values drifting over time, and short data retention time. These errors can accumulate while the accelerator is running a deep learning application, and without careful mitigation could lead to significant accuracy degradation. To assuage these concerns, this project will develop a self-healing framework for NVM-based neural network accelerators integrating a test, diagnosis, and recovery loop that monitors and maintains the health of the accelerator. Results of this project will (1) deepen the understanding of interactions among hardware defects and errors, NVM-based accelerators, and machine learning, (2) increase community awareness of post-fabrication error debugging and fixing techniques, (3) enrich the computer engineering course curriculum, and (4) train and promote students of diverse backgrounds for both the workforce and research. This project will investigate, characterize, and mitigate errors that will affect the adoption of NVM-based neural network accelerators. While existing solutions focus on fixing errors observed at fabrication time, this project targets the NVM-specific errors that will occur over the life of the accelerator, not just at the time of manufacturing. The project will lead to four outcomes, namely, (1) measurement and characterization of the error resilience capability of neural networks with different topologies and data types, (2) cost-effective approaches for deploying neural networks alongside NVM-based accelerators which exhibit new and diverse error patterns without involving costly retraining, (3) methods for generating neural network inputs as test vectors which will be tuned to be sensitive to different levels of error accumulation and accuracy loss and will provide real-time accelerator health statistics, and (4) an algorithm and device level co-diagnosis procedure which identifies and protects the most critical and vulnerable components of the neural network and the accelerator.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.

神经网络已成为解决计算机视觉，语言处理，生命科学和金融中许多实际识别和分类问题的首选工具。在有希望的同时，通过深度学习的智能和智能数据解释是极端渴望的。为了在电池受限的边缘平台上进行功率有效的深度学习，一种有前途的解决方案是使用具有新兴的非挥发性内存（NVM）设备构建的硬件加速器，这些设备提供高密度，极低的功耗以及现场和平行的数据处理。尽管这些进步令人着迷，但NVM设备也带来了额外的挑战，因为它们的设计和制造技术远不如CMAS成熟。此外，NVM技术可能会显示出新类型的错误，例如读取/写入干扰，值随时间流动以及短数据保留时间。这些错误可能会在加速器运行深度学习应用程序时积累，而无需仔细缓解可能会导致准确的降解。为了解决这些问题，该项目将为基于NVM的神经网络加速器建立一个自我修复框架，该框架集成了测试，诊断和恢复环，以监视并保持加速器的健康状况。该项目的结果将（1）加深对硬件缺陷和错误之间的相互作用，基于NVM的加速器以及机器学习的理解，（2）提高社区对工后错误调试和修复技术的认识，（3）丰富计算机工程课程课程，以及（4）（4）培训和促进多样化背景的学生，以培训和促进各种工作和研究。该项目将调查，表征和减轻会影响基于NVM的神经网络加速器的错误。尽管现有的解决方案着重于在制造时间观察到的错误，但该项目针对的是在加速器生命中会发生的NVM特定错误，而不仅仅是在制造时。该项目将导致四个结果，即（1）具有不同拓扑和数据类型的神经网络的错误复原力能力的测量和表征，（2）具有成本效益的方法，用于与基于NVM的加速器一起部署神经网络，这些加速器在基于NVM的加速器并不符合成本范围的NER网络，以符合成本的差异（3），（3）到不同程度的错误积累和准确性损失水平，并将提供实时加速器的健康统计数据，以及（4）算法和设备级别的共同诊断程序，确定并保护神经网络中最关键和最脆弱的组成部分和加速器的组成部分以及该奖项的奖励反映了NSF的法定任务和审查的范围，这是通过评估的范围来进行的。