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

职业：通过近似消息传递进行硬件加速贝叶斯推理：自下而上的方法

• 批准号: 1652065
• 负责人: Christoph Studer
• 金额: $ 60.67万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-02-15 至 2021-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1652065&HistoricalAwards=false
• 关键词: CAREER; Hardware; Accelerated; Bayesian; Inference

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.

贝叶斯推断是一种从嘈杂或损坏的测量结果中提取统计信息的有力方法。越来越多的应用依赖于实时（时间至关时间）贝叶斯推断，主要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，但实时应用程序通常依靠简单的方法来阻止使用精确的系统和信号模型。理论与实践之间的这种差异主要是由理论和算法方面的快速进步以及大多数硬件设计师的理论专业知识有限引起的。拟议的研究旨在使用跨越电路设计，算法和理论水平的整体方法来弥合理论与实践之间不断增长的差距。除了提高贝叶斯推断在实时应用程序中的效率和质量外，该项目还将通过与电信行业的协作来推动未来的无线系统，以及开发各个级别的专家都可以访问的新工具。该项目的跨学科性质也是教育外展活动中的统一主题。 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.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，算法设计师和理论家通常未知数字电路设计的限制。该项目通过采用自下而上的研究方法来解决二分法，其中硬件限制推动了算法和理论水平的努力。这种非常规的研究范式需要在各个层面上共同考虑主要挑战。特别是，该项目评估了数字电路设计关键的硬件近似值，研究算法会改变能够更有效的体系结构，并分析拟议的电路级别和算法级优化对推理复杂性和质量的影响。

项目成果

BEACHES: Beamspace Channel Estimation for Multi-Antenna mmWave Systems and Beyond

• DOI: 10.1109/spawc.2019.8815576
• 发表时间: 2019-07
• 期刊: 2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)
• 作者: Ramina Ghods;Alexandra Gallyas-Sanhueza;S. Mirfarshbafan;Christoph Studer

Linear Spectral Estimators and an Application to Phase Retrieval

线性谱估计器和相位检索的应用

• 发表时间: 2018
• 期刊: Proceedings of the 35th International Conference on Machine Learning
• 作者: Ghods, Ramina;Lan, Andrew;Goldstein, Tom;Studer, Christoph
• 通讯作者: Studer, Christoph

Sparsity-Adaptive Beamspace Channel Estimation for 1-Bit mmWave Massive MIMO Systems

• DOI: 10.1109/spawc48557.2020.9154213
• 发表时间: 2020-01-01
• 期刊: PROCEEDINGS OF THE 21ST IEEE INTERNATIONAL WORKSHOP ON SIGNAL PROCESSING ADVANCES IN WIRELESS COMMUNICATIONS (IEEE SPAWC2020)
• 作者: Gallyas-Sanhueza, Alexandra;Mirfarshbafan, Seyed Hadi;Studer, Christoph
• 通讯作者: Studer, Christoph

Non-Uniform Wavelet Sampling for RF Analog-to-Information Conversion

• DOI: 10.1109/tcsi.2017.2729779
• 发表时间: 2018-02-01
• 期刊: IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS
• 影响因子: 5.1
• 作者: Pelissier, Michael;Studer, Christoph
• 通讯作者: Studer, Christoph

Optimally-tuned nonparametric linear equalization for massive MU-MIMO systems

针对大规模 MU-MIMO 系统的优化调整非参数线性均衡

• DOI: 10.1109/isit.2017.8006903
• 发表时间: 2017
• 期刊: IEEE International Symposium on Information Theory (ISIT
• 作者: Ghods, Ramina;Jeon, Charles;Mirza, Gulnar;Maleki, Arian;Studer, Christoph
• 通讯作者: Studer, Christoph