Collaborative Research:SHF:Medium:Machine Learning on the Edge for Real-Time Microsecond State Estimation of High-Rate Dynamic Events

传统上，制造业、机器人和航空领域的动态系统的计算机控制在几十毫秒或几百毫秒的时间尺度上运行。例如，航电系统以每小时1000公里的速度运行，每次控制决策以10毫秒的速度运行，在分配给每个控制决策的时间内将移动3米。然而，新兴的高超音速、太空和军事系统需要在极端速度下运行或受到爆炸或高速碰撞引起的加速或减速时进行主动控制。这些应用程序需要在微秒级的时间尺度上进行控制。对这类系统进行控制决策通常需要控制器通过间接测量（如振动）来估计系统的状态。传统的状态预测方法基于第一性原理，采用有限元分析（FEA），其执行时间尺度为单元数的平方。这使得在微秒时间尺度上评估有限元模型变得不切实际。源自机器学习的模型可以根据预先规划的数据集估计系统的状态，与等效的有限元模型相比，所需的工作量更少。当这些模型与特定领域的处理器相结合时，可以提供与FEA模型相当的精度和更高的吞吐量，使微秒级状态建模成为可能。然而，目前还没有合适的开发方法可以在如此极端的性能约束下系统地生成机器学习模型。本研究的目标是开发满足给定精度约束的结构模型编译器，以及相应的覆盖生成器，生成的模型满足给定微秒级延迟约束。本研究将提高在经历高速率动态事件时主动结构的实时决策和控制所需的基础知识和技能。该项目解决了两个不同但协同的问题：(1)实现实时决策和控制在微秒时间尺度上经历动态事件的主动结构的技术；(2)开发用于优化和合成训练模型的特定领域处理器的工具。最近的学术和工业工作专注于开发用于评估长短期记忆（LSTM）模型的专门架构，通常产生针对特定现场可编程门阵列（FPGA）的“一次性”设计（通常是服务器级FPGA），并且具有刚性的“内置”设计决策。这使得比较备选的或竞争的优化技术为期望的目标FPGA平台变得困难。为了解决这个问题，该项目正在开发一种通用的可编程处理器体系结构，该体系结构包含一系列可选特性，旨在加速lstm的特定方面并支持相关的模型优化。该体系结构既可编程又可定制，因此可以作为评估加速LSTM模型的不同方法的通用平台。同时，研究人员正在开发一套具有精度和性能要求的结构状态估计基准数据集。该项目还为基于边缘的机器学习的后续研究开发了有用的工件，包括比较不同LSTM模型修剪和压缩方法以及比较不同微架构设计的方法。从这个项目开发的代码和硬件设计是开源的。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。

Computer control of dynamic systems from the manufacturing, robotics, and aviation fields traditionally operate on timescales of 10s or 100s of milliseconds. For example, an avionics system traveling at 1000 kilometers per hour and operating at 10 milliseconds per control decision will move three meters in the time allocated to each control decision. However, emerging hypersonic, space, and military systems require active control while operating at extreme velocities or while being subjected to accelerations or decelerations caused by explosions or high-speed collisions. These applications require control at timescales on the order of microseconds. Making control decisions for such systems often requires that the controller estimate the state of the system from indirect measurements such as vibration. Traditional methods for state prediction are based on first principles using finite element analysis (FEA), whose execution time scales as a square of the number of elements. This makes it impractical to evaluate FEA models at microsecond timescales. Models derived from machine learning can estimate the state of the system based on pre-curated datasets and require less workload as compared to an equivalent FEA model. Such models, when combined with domain-specific processors, could provide equivalent accuracy with higher throughput than FEA models, making microsecond-scale state modeling possible. However, there are currently no suitable development methodologies for systematic generation of machine-learning models at such extreme performance constraints. The objective of this research is to develop a structural model compiler that meets a given accuracy constraint, as well as a corresponding overlay generator on which the generated model meets a given microsecond-scale latency constraint. This research will advance the fundamental knowledge and skills required for the real-time decision-making and control of active structures that experience high-rate dynamic events.This project addresses two distinct but synergistic problems: (1) technologies to enable real-time decision-making and control of active structures that experience dynamic events at the microsecond timescale and (2) development of tools for optimization and synthesis of domain-specific processors for trained models. Recent academic and industrial work focusing on development of specialized architectures for evaluating Long Short Term Memory (LSTM) models generally yield “one-off” designs tuned to a specific Field Programmable Gate Array (FPGA)--often a server class FPGA--and have rigid, “baked in” design decisions. This makes it difficult to compare alternative or competing optimization techniques for a desired target FPGA platform. To solve this, this project is developing a generalized programmable processor architecture that incorporates a repertoire of optional features designed to accelerate specific aspects of LSTMs and support associated model optimizations. The architecture is both programmable and customizable, allowing it to serve as a common platform for evaluating different approaches for accelerating LSTM models. Concurrently, the investigators are developing a set of benchmark datasets for structural state estimation with accuracy and performance requirements. The project is also developing useful artifacts for subsequent research in edge-based machine learning, including a method for comparing different LSTM model-pruning and compression approaches and comparing different microarchitecture designs. Code and hardware designs developed from this project are open-source.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.