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

机器学习的最新进展正在推动对智能物联网(IoT)，即边缘网络应用的日益增长的需求。其中许多设备，如自动驾驶汽车、机器人和医疗可穿戴设备，都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(如移动设备、传感器)可用的有限计算和能源资源与最先进的机器学习培训的巨大且不断增长的成本存在冲突，这对边缘设备的实时机器学习(RTML)构成了巨大的挑战。该项目的目标是促进在普及的资源受限的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培训方面的系统性突破。RTML的数量级进步将使许多边缘设备能够主动解释新数据并从新数据中学习，利用所学知识改进自己的性能，并实时适应动态环境。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，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。该项目采用三管齐下的“协同设计”方法，无缝集成了算法、架构和电路级创新，解决了RTML的重大挑战。具体地说，在算法层面，将建立一个有效的RTML训练框架，对其训练的模型也是天生有效的推理。首先，通过改进一般训练技术，然后特别注重在线学习和适应，可以大幅减少时间和精力。在架构层面，该项目将首先通过将数据移动的高成本交换为低成本计算来降低高成本，然后生成最佳数据流和硬件架构，以最大限度地发挥算法和硬件的共同优势。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、可扩展和健壮的机器学习芯片。最后，在系统层面，将包括一项整合努力，以帮助实现现实的系统目标，并评估三个核心推动的创新。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。

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.