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: 1937435
• 负责人: Yiran Chen
• 金额: $ 25万
• 依托单位: Duke University
• 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 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1937435&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正在推动对智能互联网（IoT）（即边缘网络应用程序）的不断增长的需求。其中许多人，例如自动驾驶汽车，机器人和医疗保健可穿戴设备，都需要实时和原位学习才能被认为是真正聪明的。但是，在边缘设备（例如，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成本矛盾，对Edge的实时机器学习（RTML）构成了巨大的挑战。该项目的这个目标是在对普遍存在的资源约束平台和应用程序中的最先进的机器学习算法进行有效的在线培训方面取得系统的突破。 RTML中的一个数量级进步将使众多边缘设备能够从新数据中主动解释和学习，使用他们学到的知识来提高自己的性能，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能够穿透各行各业，从而对社会和经济产生重大影响。该项目将导致新课程和开放式教育资源，这些资源可以吸引各种学生，并最终提供一个包容和创新的平台。 该项目使用三管齐下的“共同设计”方法解决了RTML大挑战，该方法无缝地集成了算法，建筑和电路级创新。具体而言，在算法级别上，RTML有效的训练框架将建立训练有素的模型也是如此有效的推理。首先，可以通过改进一般培训技术，然后专门针对在线学习和适应来实现积极的时间和减少能量。在体系结构级别，该项目将首先定位通过将其交易以降低成本计算，然后生成最佳数据流和硬件体系结构来最大程度地利用算法和硬件的关节优势，从而降低了数据移动的高成本。在电路级别，该项目将利用自适应的低精度算法和体系结构来设计超能量的混合信号计算织物。统计计算技术将合并，以证明有效，可扩展和健壮的机器学习芯片。最后，在系统层面上，将包括一项集成努力，以帮助实现现实的系统目标并评估三个核心力量的创新。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点评估来支持的，并具有更广泛的影响标准。

项目成果

PENNI: Pruned Kernel Sharing for Efficient CNN Inference

• 发表时间: 2020-05
• 期刊: ArXiv
• 作者: Shiyu Li;Edward Hanson;H. Li;Yiran Chen

DyNNamic: Dynamically Reshaping, High Data-Reuse Accelerator for Compact DNNs

• DOI: 10.1109/tc.2022.3184272
• 发表时间: 2023-03
• 期刊: IEEE Transactions on Computers
• 影响因子: 3.7
• 作者: Edward Hanson;Shiyu Li;Xuehai Qian;H. Li;Yiran Chen

NASRec: Weight Sharing Neural Architecture Search for Recommender Systems

NASRec：推荐系统的权重共享神经架构搜索

• DOI: 10.1145/3543507.3583446
• 发表时间: 2023
• 期刊: the ACM Web Conference 2023
• 作者: Zhang, Tunhou;Cheng, Dehua;He, Yuchen;Chen, Zhengxing;Dai, Xiaoliang;Xiong, Liang;Yan, Feng;Li, Hai;Chen, Yiran;Wen, Wei
• 通讯作者: Wen, Wei

PIDS: Joint Point Interaction-Dimension Search for 3D Point Cloud

PIDS：3D 点云的联合点交互维度搜索

• 发表时间: 2023
• 期刊: EEE/CVF Winter Conference on Applications of Computer Vision
• 作者: Zhang, Tunhou;Ma, Mingyuan;Yan, Feng;Li, Hai;Chen, Yiran
• 通讯作者: Chen, Yiran

ESCALATE: Boosting the Efficiency of Sparse CNN Accelerator with Kernel Decomposition

• DOI: 10.1145/3466752.3480043
• 发表时间: 2021-10
• 期刊: MICRO-54: 54th Annual IEEE/ACM International Symposium on Microarchitecture
• 作者: Shiyu Li;Edward Hanson;Xuehai Qian;H. Li;Yiran Chen