RTML: Large: Collaborative: Harmonizing Predictive Algorithms and Mixed-Signal/Precision Circuits via Computation-Data Access Exchange and Adaptive Dataflows

RTML：大型：协作：通过计算数据访问交换和自适应数据流协调预测算法和混合信号/精密电路

• 批准号: 1937588
• 负责人: Zhangyang Wang
• 金额: $ 24.85万
• 依托单位: Texas A&M Engineering Experiment Station
• 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 2020-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1937588&HistoricalAwards=false
• 关键词: RTML; Large; Collaborative; Harmonizing; Predictive

Recent advances in machine learning are fueling a growing demand for intelligent Internet of Things (IoT), i.e., edge network applications. Many of them, such as autonomous vehicles, robots, and healthcare wearables, require real-time and in-situ learning to be perceived as truly intelligent. However, the limited computing and energy resources available at the edge device (e.g., mobile devices, sensors) stand at odds with the massive and growing cost of state-of-the-art machine learning training, posing a grand challenge for real-time machine learning (RTML) at the edge. This goal of this project is to foster a systematic breakthrough in achieving efficient online training of state-of-the-art machine learning algorithms in pervasive resource-constrained platforms and applications. An order of magnitude advance in RTML would enable numerous edge devices to proactively interpret and learn from new data, improve their own performance using what they have learned, and adapt to dynamic environments, all in real time. Success in this project will enable truly intelligent edge devices to penetrate all walks of life and thus generate significant impacts on societies and economies. This project will lead to new courses and open-education resources that can attract diverse groups of students and eventually deliver a platform for inclusion and innovation. The project addresses the RTML grand challenge using a three-pronged 'co-design' approach that seamlessly integrates algorithm, architecture, and circuit-level innovations. Specifically, at the algorithm level, an efficient training framework for RTML, for which trained models are also natively efficient for inference, will be established. Aggressive time and energy reductions can be achieved, at first by improving general training techniques, and then by focusing particularly on online learning and adaptation. At the architecture level, the project will first target reducing the high cost of data movement by trading it for lower-cost computation, and then generate optimal dataflows and hardware architectures to maximize the joint benefits of algorithms and hardware. At the circuit level, the project will leverage adaptive low-precision algorithms and architectures to design ultra-energy-efficient mixed-signal compute fabrics. Statistical computing techniques will be incorporated to demonstrate efficient, scalable, and robust machine learning chips. Finally, at the system level, an integration effort will be included to aid the realization of realistic system goals and to evaluate the innovations of the three core thrusts.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.

机器学习的最新进展推动了对智能物联网（IoT）（即边缘网络应用）日益增长的需求。其中许多产品，例如自动驾驶汽车、机器人和医疗保健可穿戴设备，需要实时和现场学习才能被视为真正的智能。然而，边缘设备（例如移动设备、传感器）可用的计算和能源资源有限，与最先进的机器学习训练的巨大且不断增长的成本相矛盾，这对实时性提出了巨大的挑战边缘机器学习 (RTML)。该项目的目标是促进系统性突破，在普遍的资源受限平台和应用程序中实现最先进的机器学习算法的高效在线训练。 RTML 的一个数量级进步将使众多边缘设备能够主动解释和学习新数据，利用所学知识提高自身性能，并适应动态环境，所有这些都是实时的。该项目的成功将使真正的智能边缘设备渗透到各行各业，从而对社会和经济产生重大影响。该项目将带来新的课程和开放教育资源，可以吸引不同的学生群体，并最终提供一个包容和创新的平台。 该项目使用无缝集成算法、架构和电路级创新的三管齐下的“协同设计”方法来应对 RTML 的巨大挑战。具体来说，在算法层面，将建立一个高效的RTML训练框架，训练后的模型本身也能高效地进行推理。首先通过改进一般培训技术，然后特别关注在线学习和适应，可以大幅减少时间和精力。在架构层面，该项目将首先以降低数据移动的高成本为目标，通过以更低成本的计算来换取数据移动的高成本，然后生成最佳的数据流和硬件架构，以最大限度地发挥算法和硬件的联合优势。在电路层面，该项目将利用自适应低精度算法和架构来设计超节能的混合信号计算结构。将结合统计计算技术来展示高效、可扩展且强大的机器学习芯片。最后，在系统层面，将包括整合工作，以帮助实现现实的系统目标，并评估三个核心主旨的创新。该奖项反映了 NSF 的法定使命，并通过使用基金会的评估进行评估，认为值得支持。智力价值和更广泛的影响审查标准。

项目成果

E2-train: Training state-of-the-art CNNs with over 80% energy savings

E2-train：%20训练%20最先进的%20CNNs%20with%20over%2080%%20energy%20节省

• 发表时间: 2019-12
• 期刊: Advances in Neural Information Processing Systems 32 (NIPS 2019
• 作者: Wang, Yue;Jiang, Ziyu;Chen, Xiaohan;Xu, Pengfei;Zhao, Yang;Lin, Yingyan;Wang, Zhangyang
• 通讯作者: Wang, Zhangyang

Drawing Early-Bird Tickets: Toward More Efficient Training of Deep Networks

抢早鸟票：实现更高效的深度网络训练

• 发表时间: 2020-05
• 期刊: 2019
• 作者: You, Haoran;Li, Chaojian;Xu, Pengfei;Fu, Yonggan;Wang, Yue;Chen, Xiaohan;Baraniuk, Richard G.;Wang, Zhangyang;Lin, Yingyan
• 通讯作者: Lin, Yingyan