State Estimation Algorithms on Computer Architectures that Track Uncertainty

跟踪不确定性的计算机体系结构的状态估计算法

• 批准号: 2597692
• 金额: --
• 依托单位: University of Cambridge
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2597692
• 关键词: State; Estimation; Algorithms; Computer; Architectures

Uncertainty is common in computing systems. Computers interact with their environment and since data originates from the environment, it contains uncertainty or noise. Uncertainty also comes up from computation itself, as in floating-point arithmetic or neural network training weights.State estimation algorithms, such as the Kalman filters or the particle filters, can track the evolution of dynamic processes in the presence of uncertainty. For example, a self-regulating control system, such as a robotic arm, a micro-UAV, or a thermostat, often must act based on uncertain inputs from, e.g., noisy measurements from the system sensors. Using models of the process and its relation to the sensor measurements, these algorithms use uncertainty representations to leverage trust between the model's predictions and the sensor readings to produce an uncertainty-minimizing estimate of the process state which leads to more robust control.In general, contemporary software design leaves dealing with uncertainty as an afterthought. Engineering systems that withstand uncertainty is hard and cumbersome, and no general solutions have emerged for it. Computer architectures that track uncertainty can present a solution to this problem. They can provide the necessary abstractions to track uncertainty and convenient interfaces to design software around them. Such architectures can benefit solutions to state estimation, a problem directly related to tracking uncertainty. Yet, there are no established hardware abstractions, compilers, tools and programming languages for this architecture paradigm. There are also no universal and tractable solutions for tracking correlation in such architectures.This project will investigate new algorithms for state estimation for computer architectures that track uncertainty. This will lead to new insights into how such computer architectures can influence the design and implementation of state estimation algorithms. This research will investigate concise representations for uncertainty in computation, techniques for microarchitecture-assisted Bayesian inference and correlation tracking, compiler optimizations for these computer architectures, and programming language constructs and paradigms for programming on these architectures, to achieve high-level support for programming with probabilistic data, while exploiting recent advances in probabilistic programming.

不确定性在计算系统中很常见。计算机与其环境交互，由于数据源自环境，因此包含不确定性或噪声。不确定性也来自计算本身，如浮点运算或神经网络训练权重。状态估计算法，如卡尔曼滤波或粒子滤波，可以在存在不确定性的情况下跟踪动态过程的演变。例如，自调节控制系统，例如机械臂、微型无人机或恒温器，通常必须基于来自例如来自系统传感器的噪声测量的不确定输入而动作。通过使用过程模型及其与传感器测量的关系，这些算法使用不确定性表示来利用模型预测和传感器读数之间的信任，以产生过程状态的不确定性最小化估计，从而导致更稳健的控制。通常，当代软件设计将处理不确定性留在事后考虑。经受住不确定性的工程系统是困难和繁琐的，而且还没有出现通用的解决方案。跟踪不确定性的计算机体系结构可以为这个问题提供解决方案。它们可以提供必要的抽象来跟踪不确定性，并提供方便的接口来围绕它们设计软件。这样的架构可以帮助解决状态估计问题，这是一个与跟踪不确定性直接相关的问题。然而，对于这种体系结构范例，还没有既定的硬件抽象、编译器、工具和编程语言。在这样的体系结构中也没有通用和易处理的跟踪相关性的解决方案。本项目将研究用于跟踪不确定性的计算机体系结构的状态估计的新算法。这将导致对这种计算机体系结构如何影响状态估计算法的设计和实现的新见解。这项研究将研究计算中不确定性的简明表示，微体系结构辅助贝叶斯推理和相关性跟踪的技术，这些计算机体系结构的编译器优化，以及在这些体系结构上编程的编程语言结构和范例，以实现对使用概率数据进行编程的高级支持，同时利用概率编程的最新进展。