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

神经网络已经成为解决计算机视觉、语言处理、生命科学和金融领域许多现实世界识别和分类问题的首选工具。虽然很有前途，但通过深度学习进行智能和智能的数据解释非常耗电。为了在电池有限的边缘平台上进行节能深度学习，一个很有前途的解决方案是使用使用新兴非易失性存储器（NVM）设备构建的硬件加速器，这种设备提供高密度、极低功耗以及原位和并行数据处理。虽然这些进步很诱人，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CMOS成熟。此外，NVM技术可能会出现新的错误类型，例如读/写干扰、值随时间漂移和数据保留时间短。当加速器运行深度学习应用程序时，这些错误可能会累积，如果不小心加以缓解，可能会导致精度显著降低。为了缓解这些担忧，该项目将为基于nvm的神经网络加速器开发一个自我修复框架，该框架集成了测试、诊断和恢复回路，以监测和维护加速器的健康状况。该项目的成果将(1)加深对硬件缺陷和错误、基于nvm的加速器和机器学习之间相互作用的理解，(2)提高社区对制造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，(4)培养和促进不同背景的学生从事工作和研究。该项目将调查、描述和减轻影响采用基于nvm的神经网络加速器的错误。虽然现有的解决方案侧重于修复在制造时观察到的错误，但该项目的目标是在加速器的整个生命周期中发生的nvm特定错误，而不仅仅是在制造时。该项目将产生四个结果，即：(1)测量和表征具有不同拓扑和数据类型的神经网络的错误恢复能力；(2)将神经网络与基于nvm的加速器一起部署的成本效益方法，这些加速器显示出新的和多样化的错误模式，而无需进行昂贵的再培训。(3)生成神经网络输入作为测试向量的方法，该方法将被调整为对不同级别的错误积累和准确性损失敏感，并将提供实时加速器健康统计数据；(4)一种算法和设备级联合诊断程序，用于识别和保护神经网络和加速器中最关键和最脆弱的组件。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。

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.