State Estimation Algorithms on Computer Architectures that Track Uncertainty

不确定性在计算系统中很常见。计算机与环境相互作用，由于数据来源于环境，因此它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值。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，如机械臂、微型无人机或恒温器，通常必须基于来自系统传感器的噪声测量等不确定输入进行操作。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，从而产生过程状态的不确定性最小化估计，从而实现更稳健的控制。一般来说，当代软件设计将处理不确定性作为事后的想法。能够承受不确定性的工程系统既困难又繁琐，目前还没有通用的解决方案。跟踪不确定性的计算机体系结构可以为这个问题提供一个解决方案。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。这样的架构有利于状态估计的解决方案，这是一个与跟踪不确定性直接相关的问题。然而，目前还没有针对这种架构范例的硬件抽象、编译器、工具和编程语言。在这样的体系结构中，也没有通用的和可处理的解决方案来跟踪相关性。该项目将研究跟踪不确定性的计算机体系结构状态估计的新算法。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。本研究将探讨计算中不确定性的简明表示、微架构辅助贝叶斯推理和相关跟踪技术、这些计算机体系结构的编译器优化以及这些体系结构上编程的编程语言结构和范式，以实现对概率数据编程的高级支持，同时利用概率编程的最新进展。

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.