SHF: Small: Collaborative Research: Software Hardware Architecture Co-design for Low-power Heterogeneous Edge Devices

深度学习技术是机器学习的一个子领域，它的发展正在深刻地改变移动边缘计算领域，这要归功于最近的研究表明，深度学习方法可以提供显著的性能提升。然而，繁重的计算和资源要求阻碍了深度学习方法在智能手机和物联网(IoT)设备等移动边缘设备中的广泛部署。在移动边缘设备中启用深度学习方法的一个显著优势是，由于计算是在本地执行的，因此它可以大幅降低移动应用的响应延迟和能耗。通过消除深度学习技术远离无处不在的低功耗移动边缘计算设备的障碍，这项研究使高精度、低延迟的应用在未来的移动边缘计算中成为可能。特别是，本研究系统地研究了在保证性能的情况下显著降低移动边缘设备的深度学习推理过程的成本的基础性和挑战性问题。该项目的成功将极大地惠及各个研究领域的整个深度学习领域，包括计算机体系结构、移动传感、网络安全和人机交互研究领域。该项目还旨在开发新的课程，并鼓励工程专业的女学生参与。这项研究的主要目标是构建一个软件加速器，使高成本的深度学习模型能够广泛部署到资源受限的异类移动边缘设备(例如低成本传感平台和物联网设备)。其基本思想是开发深度学习资源管理算法，能够根据不同边缘设备的硬件约束，调整不同深度学习模型的结构。更具体地说，本研究分析了移动边缘设备上不同的深度学习行为，并设计了不同的策略来提高基于深度学习的多个推理模型的效率。此外，本研究还开发了可以调整不同深度学习模型的复杂度的算法，以降低其在移动边缘设备上的能量和内存消耗。此外，该项目设计了以电力为中心的资源重新分配算法，以验证和部署移动友好型深度学习模型。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The advancement of deep learning techniques, a sub-field of machine learning, is profoundly changing the field of mobile edge computing, thanks to recent research demonstrating that deep learning methods provide significant performance gains. However, the requirement of heavy computations and resources prevent deep learning methods from being widely deployed in mobile edge devices, such as smartphones and Internet of Things (IoT) devices. A significant advantage of enabling deep learning methods in mobile edge devices is that it can drastically reduce the response delay and energy consumption of mobile applications because the computations are executed locally. By removing the barrier that keeps deep learning techniques away from pervasive low-power mobile edge computing devices, this research enables high-accuracy, low-latency applications in future mobile edge computing. In particular, this research systematically investigates the fundamental and challenging issues targeting to significantly reduce the cost of deep learning inference process in mobile edge devices with guaranteed performance. The success of this project could significantly benefit the entire spectrum of deep learning across various research domains, including computer architecture, mobile sensing, cyber security, and human-computer interaction research areas. This project also aims to develop new curricula and encourage the participation of female engineering students. The primary goal of this research is to build a software accelerator that enables the broad deployment of heavy-cost deep learning models into resource-constrained, heterogeneous mobile edge devices (e.g., low-cost sensing platforms and IoT devices). The basic idea is to develop deep-learning resource management algorithms that can adjust structures of different deep learning models according to hardware constraints of heterogeneous edge devices. More specifically, this research analyzes distinct deep learning behaviors on mobile edge devices and designs different strategies to improve the efficiency of multiple deep-learning-based inference models. Furthermore, this research develops algorithms that can adjust the complexity of different deep learning models to reduce their energy and memory consumption on mobile edge devices. In addition, this project designs power-centric resource reallocation algorithms to verify and deploy the mobile-friendly deep learning models.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.