Collaborative Research: PPoSS: Planning: Hardware-accelerated Trustworthy Deep Neural Network

深度学习方法最近在各种应用中实现了比传统机器学习方法高得多的准确性（例如，计算机视觉、虚拟/增强现实和自然语言处理）。现有的研究表明，来自各种来源的具有高分辨率传感或大容量数据收集能力的大规模数据可以显着提高深度学习方法的性能。然而，最先进的硬件和软件无法提供足够的计算能力和资源，以确保在使用超大规模数据时及时实现准确的深度学习性能。该项目开发了一个可扩展且强大的异构系统，包括一个新的低成本，安全的深度学习硬件加速器架构和一套大数据兼容的深度学习算法。它允许深度学习充分受益于超大规模数据，并促进互联车辆中的高效、低延迟应用、实时移动的应用和及时的精准健康。该项目产生的新技术可以为设计用于深度学习的新硬件加速器提供更多的研究机会，并进一步优化计算复杂度和降低功耗。此外，通过将研究成果与本科生和研究生课程以及推广活动相结合，该项目对计算机体系结构、安全、理论和算法以及系统的研究人员和工程师的教育和培训产生了重大影响。该项目设计了值得信赖的硬件加速器，为大规模深度学习计算进行了优化，并对大规模数据集的复杂结构进行了建模。更具体地说，该项目开发了一种用于深度学习的新型硬件加速器，可以实现低功耗。此外，该项目还设计了创新的内存加密方案，以保护深度学习加速器中的神经模型。此外，该项目还开发了数据建模和自动学习算法，以进一步降低深度学习在处理超大规模数据集时的计算成本。最后，该项目构建并评估了拟议的异构深度学习系统原型，在多个应用领域（包括移动的应用、联网车辆和精确健康）中的效率、可扩展性和安全性。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

Deep-learning approaches have recently achieved much higher accuracy than traditional machine-learning approaches in various applications (e.g., computer vision, virtual/augmented reality, and natural language processing). Existing research has shown that large-scale data from various sources with high-resolution sensing or large-volume data-collection capabilities can significantly improve the performance of deep-learning approaches. However, state-of-the-art hardware and software cannot provide sufficient computing capabilities and resources to ensure accurate deep-learning performance in a timely manner when using extremely large-scale data. This project develops a scalable and robust heterogeneous system that includes a new low-cost, secure, deep-learning hardware-accelerator architecture and a suite of large-data-compatible deep-learning algorithms. It allows deep learning to fully benefit from extremely large-scale data and facilitates efficient, low-latency applications in connected vehicles, real-time mobile applications, and timely precision health. The new technologies resulting from this project can enable more research opportunities to design new hardware accelerators for deep learning and obtain further optimization in computational complexity and reduction in power consumption. Moreover, by integrating the research results with the undergraduate and graduate curricula and outreach activities, this project has great impacts on education and training of researchers and engineers for computer architecture, security, theory and algorithms, and systems.This project designs trustworthy hardware accelerators optimized for large-scale deep-learning computations and models the complicated structure of large-scale datasets. More specifically, this project develops a novel hardware accelerator for deep learning that can achieve low power consumption. In addition, this project designs innovative in-memory encryption schemes to secure the neural models in deep-learning accelerators. Furthermore, data-modeling and statistical-learning algorithms are developed in this project to further reduce the computing cost of deep learning when processing extremely large-scale datasets. Finally, this project builds and evaluates a prototype of the proposed heterogeneous deep-learning system in terms of efficiency, scalability, and security in multiple application domains including mobile applications, connected vehicles and precision health.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.