State Estimation Algorithms on Computer Architectures that Track Uncertainty

不确定性在计算系统中很常见。计算机与其环境交互，由于数据来自环境，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重。状态估计算法，如卡尔曼滤波器或粒子滤波器，可以跟踪存在不确定性的动态过程的演变。例如，诸如机器人臂、微型UAV或恒温器的自调节控制系统通常必须基于来自例如以下的不确定输入来动作：来自系统传感器的噪声测量。这些算法使用过程模型及其与传感器测量值的关系，利用不确定性表示来利用模型预测与传感器读数之间的信任，以产生对过程状态的不确定性最小化估计，从而实现更鲁棒的控制。工程系统承受不确定性是困难和繁琐的，并没有通用的解决方案出现，计算机架构跟踪不确定性可以提出一个解决方案，这个问题。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。这样的架构可以有益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，还没有建立硬件抽象，编译器，工具和编程语言，这种架构范例。也没有通用的和易于处理的解决方案来跟踪相关性在这样的架构。这个项目将研究新的算法，用于状态估计的计算机架构，跟踪不确定性。这将导致新的见解，如何这样的计算机架构可以影响状态估计算法的设计和实现。本研究将调查简明表示计算中的不确定性，技术微架构辅助贝叶斯推理和相关跟踪，编译器优化这些计算机架构，和编程语言的结构和范式编程这些架构，以实现高层次的支持与概率数据编程，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.