RTML: Large: Efficient and Adaptive Real-Time Learning for Next Generation Wireless Systems

实现更快的数据速率和超低延迟的需求推动了新兴的无线标准和5G通信的前景。许多令人难以置信的前沿应用，如社区/共享虚拟现实体验和自动驾驶汽车，关键依赖于无处不在的高速无线链路的可用性和实时可重构性，这反过来又强烈依赖于下一代无线设备实时执行各种推断任务的能力。与这些应用程序相关的延迟要求意味着需要通过专用硬件改进和加速机器学习。此外，由于无线信道的不可预测性，推理算法必须能够在不熟悉的环境中适应和发展。该项目旨在解决这一基础挑战，成功的成果能够在下一代无线系统中实现前所未有的效率提高。该项目的想法和发现被纳入了一系列面向高中和本科生的研讨会，以鼓励对工程和科学的进一步兴趣，以及通过针对无线网络和机器学习社区的多学科教程。该项目由机器学习、系统和网络研究人员组成的多学科团队执行，通过针对与下一代无线设备相关的推理任务量身定制的新颖深度学习架构，推进了最先进的技术。它还结合了新的模型压缩技术，为深度神经网络的全连接和卷积层提供了一种硬件友好的结构化修剪方法，并结合了在训练过程中共同学习的新型量化方案。该项目的量化方案及其超参数调谐是与现场可编程门阵列（FPGA）硬件实现共同设计的，并通过深度强化学习确定。通过混合终身学习方法（如动态网络和互补学习）作为训练期间的新目标，可以实现网络部分在新样本存在下的自适应。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。

Emerging wireless standards and the promise of 5G communication are driven by the need to attain faster data rates and ultra-low latency. Many incredible bleeding-edge applications, such as community/shared virtual reality experiences and self-driving cars, crucially rely on the ubiquitous availability and real-time reconfigurability of high-speed wireless links, which in turn strongly relies on the ability of next-generation wireless devices to perform a broad variety of inference tasks in real-time. The latency requirements associated with these applications imply the need for improved and accelerated machine learning through dedicated hardware. Moreover, due to the unpredictable nature of the wireless channel, inference algorithms must be able to adapt and evolve in the presence of an unfamiliar environment. This project seeks to solve this foundational challenge, with successful outcomes being able to achieve unprecedented efficiency improvements in next generation wireless systems. Ideas and findings from the project are incorporated into a number of accessible seminar talks geared at high-school and undergraduate students, to encourage further interest in engineering and science, as well as through multi-disciplinary tutorials aimed at both the wireless networking and machine learning communities.The project, executed by a multidisciplinary team of machine learning, systems, and networking researchers, advances the state of the art through novel deep learning architectures tailored to inference tasks pertinent to next generation wireless devices. It also incorporates novel model compression techniques, producing a hardware-friendly structured pruning approach for fully-connected and convolutional layers of deep neural networks, combined with a novel quantization scheme learned jointly during training. The project's quantization scheme and its hyper-parameter tuning is co-designed with an field programmable gate array (FPGA) hardware implementation and determined via deep reinforcement learning. The adaptation of parts of the network in the presence of new samples is enabled by blending lifelong learning approaches like dynamic networks and complementary learning as new objectives during training.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.