CAREER: Hardware Accelerated Bayesian Inference via Approximate Message Passing: A Bottom-Up Approach

贝叶斯推理是一种从噪声或损坏的测量中提取统计信息的有效方法。越来越多的应用依赖于实时（时间关键）贝叶斯推理，主要是在无线通信和成像领域。虽然最复杂的算法是为时间不敏感的任务而设计的，但实时应用通常依赖于简单的方法，从而无法使用准确的系统和信号模型。这种理论与实践的差距主要是由于理论和算法方面的快速发展以及大多数硬件设计师的理论专业知识有限造成的。本研究旨在透过电路设计、演算法及理论层面的整体方法，弥合理论与实务之间日益扩大的差距。除了提高贝叶斯推理在实时应用中的效率和质量外，该项目还将通过与电信行业的合作，以及开发可供各级专家使用的新工具，推进未来的无线系统。这个项目的跨学科性质也是整个教育推广活动的统一主题。PI将为未被充分代表的少数族裔高中学生提供实践设计课程，并将指导来自南美的本科生，目的是增加跨学科研究的参与。该项目建立在近似消息传递（AMP）的基础上，这是一个强大的统计框架，有助于设计有效的算法，并配备了用于表征推理复杂性和质量的分析工具。不幸的是，AMP背后的理论使大多数电路设计师无法访问；同样，数字电路设计的约束通常是算法设计者和理论家所不知道的。这个项目通过追求一种自下而上的研究方法解决了这种二分法，在这种方法中，硬件限制推动了算法和理论层面的努力。这种非传统的研究范式需要综合考虑各个层面的重大挑战。特别是，该项目评估了数字电路设计的关键硬件近似，研究了实现更高效架构的算法转换，并分析了所提出的电路级和算法级优化对推理复杂性和质量的影响。

Bayesian inference is a powerful method for extracting statistical information from noisy or corrupted measurements. A growing number of applications relies on real-time (time-critical) Bayesian inference, mainly in the fields of wireless communications and imaging. While the most sophisticated algorithms have been designed for time-insensitive tasks, real-time applications typically rely on simplistic methods that prevent the use of accurate system and signal models. This disparity between theory and practice is mainly caused by the fast progress on the theory and algorithm side and the limited theoretical expertise of most hardware designers. The proposed research aims to bridge the ever-growing gap between theory and practice using a holistic approach that spans the circuit design, algorithm, and theory levels. In addition to improving the efficiency and quality of Bayesian inference in real-time applications, the project will advance future wireless systems through collaboration with the telecommunications industry, along with the development of new tools that are accessible to experts on all levels. The interdisciplinary nature of this project is also the unifying theme across the educational outreach activities. The PI will lead hands-on design sessions for underrepresented minority high-school students and will supervise undergraduates from South America with the goal of increasing participation in interdisciplinary research.The project builds upon approximate message passing (AMP), a powerful statistical framework that facilitates the design of efficient algorithms and is equipped with analytical tools for characterizing inference complexity and quality. Unfortunately, the theory behind AMP makes it inaccessible to most circuit designers; similarly, the constraints of digital circuit design are generally unknown to algorithm designers and theorists. This project resolves the dichotomy by pursuing a bottom-up research approach in which hardware limitations drive efforts on the algorithm and theory levels. This unconventional research paradigm requires a joint consideration of the major challenges on all levels. In particular, the project evaluates hardware approximations that are key for digital circuit designs, investigates algorithm transforms that enable more efficient architectures, and analyzes the impacts of the proposed circuit-level and algorithm-level optimizations on the inference complexity and quality.