FET: Small: Smart Probabilistic Computation with Limited Resources

FET：小型：资源有限的智能概率计算

• 批准号: 2006704
• 负责人: John Hayes
• 金额: $ 49.21万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-15 至 2024-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2006704&HistoricalAwards=false
• 关键词: FET; Small; Smart; Probabilistic; Computation

The overall goal of this project is to understand how to build smarter, more compact, and more environmentally friendly computing devices that can cope efficiently with uncertainty. Many current computer-based machines, for example, mobile phones and advanced biomedical devices, depend on remote data centers (the so-called cloud) to provide brain-like computational services that employ artificial intelligence and are probabilistic in nature. These centers consume enormous amounts of high-cost resources such as computer hardware, computing time, and electrical energy. Embedding their functions directly in low-cost, reliable and environmentally friendly ways in end-user devices like smartphones is a key research challenge with broad implications. One way of doing this is the stochastic computing (SC) approach explored in this project. The SC circuits are inherently probabilistic and are orders-of-magnitude smaller and more error-tolerant than conventional circuits. Thus, they provide novel ways to create tiny, low-cost and reliable systems. Besides its potential for reducing the cost of, and need for, large systems like data centers, SC has recently been shown suitable for extremely resource-restricted biomedical applications such as retinal implants to restore vision to the blind. The project will also enable graduate, undergraduate and underrepresented students to be trained in computing techniques at the forefront of modern computing technologies, thus contributing to the future workforce.Traditional computer science is mostly devoted to non-probabilistic (deterministic) methods that, although far easier to understand than probabilistic, are less well-suited to the applications of interest cited above. Stochastic computing is one of the most promising probabilistic techniques available. It can be concisely defined as computing with probabilities encoded in randomized streams of 0’s and 1’s. It is readily implementable with standard electronic technology; for example, a single logic gate can perform multiplication, even when the input data is very noisy. The project will investigate the theoretical underpinnings of SC and its application to severely resource-restricted systems. Among the questions to be addressed are: What are the computational fundamental limits on SC’s accuracy? How can SC’s unique progressive precision and randomness properties be exploited? How can system architects efficiently balance accuracy, performance, energy needs, and overall system cost? Prototype systems will be designed and evaluated via mathematical analysis, simulation and emulation using field programmable gate arrays. These include designs incorporating neural networks, as well as smart cameras, medical implants, and portable automatic speech-recognition systems. The research is also expected to provide useful insights into other probabilistic technologies such as quantum computing.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目的总体目标是了解如何构建更智能、更紧凑、更环保的计算设备，从而有效地应对不确定性。目前许多基于计算机的机器，例如手机和先进的生物医学设备，都依赖于远程数据中心（所谓的云）来提供类似大脑的计算服务，这些服务采用人工智能，本质上是概率性的。这些中心消耗大量的高成本资源，如计算机硬件、计算时间和电能。将它们的功能以低成本、可靠和环保的方式直接嵌入智能手机等终端用户设备是一项具有广泛意义的关键研究挑战。其中一种方法是本项目中探索的随机计算（SC）方法。SC电路本质上是概率性的，比传统电路小几个数量级，容错性更强。因此，它们提供了创造微小、低成本和可靠系统的新方法。除了具有降低数据中心等大型系统成本和需求的潜力外，SC最近还被证明适用于资源极其有限的生物医学应用，例如用于恢复盲人视力的视网膜植入物。该项目还将使研究生、本科生和代表性不足的学生能够在现代计算机技术的最前沿接受计算机技术培训，从而为未来的劳动力做出贡献。传统的计算机科学主要致力于非概率（确定性）方法，尽管这些方法比概率方法容易理解得多，但不太适合上述感兴趣的应用。随机计算是最有前途的概率技术之一。它可以被简洁地定义为用0和1随机流编码的概率计算。它很容易实现与标准的电子技术；例如，即使输入数据噪声很大，单个逻辑门也可以执行乘法。该项目将研究SC的理论基础及其在资源严重受限系统中的应用。需要解决的问题包括：SC的准确性在计算上的基本限制是什么？如何利用SC独特的渐进式精度和随机性属性？系统架构师如何有效地平衡准确性、性能、能源需求和总体系统成本？原型系统将通过使用现场可编程门阵列的数学分析、仿真和仿真来设计和评估。其中包括结合神经网络、智能摄像头、医疗植入物和便携式自动语音识别系统的设计。这项研究还有望为量子计算等其他概率技术提供有用的见解。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Exploring Target Function Approximation for Stochastic Circuit Minimization

• DOI: 10.1145/3400302.3415701
• 发表时间: 2020-11
• 期刊: 2020 IEEE/ACM International Conference On Computer Aided Design (ICCAD)
• 作者: Chen Wang;Weihua Xiao;J. Hayes;Weikang Qian

CeMux: Maximizing the Accuracy of Stochastic Mux Adders and an Application to Filter Design

CeMux：最大化随机复用加法器的精度及其在滤波器设计中的应用

• DOI: 10.1145/3491213
• 发表时间: 2022
• 期刊: ACM Transactions on Design Automation of Electronic Systems
• 影响因子: 1.4
• 作者: Baker, Timothy J.;Hayes, John P.
• 通讯作者: Hayes, John P.

Multiplexer-majority chains: managing correlation and cost in stochastic number generation

多路复用器多数链：管理随机数生成中的相关性和成本

• 发表时间: 2022
• 期刊: 17th Symposium on Nanoscale Architectures (NANOARCH
• 作者: Baker, Timothy J.;Hoffend, Owen;Hayes, John P.
• 通讯作者: Hayes, John P.

Wavelet Transform Assisted Neural Networks for Human Activity Recognition

用于人类活动识别的小波变换辅助神经网络

• 发表时间: 2022
• 期刊: International Symposium on Circuits & Systems (ISCAS
• 作者: Sengupta, Roshwin;Polian, Ilia;Hayes, John P.
• 通讯作者: Hayes, John P.

Analyzing Multilevel Stochastic Circuits using Correlation Matrices

使用相关矩阵分析多级随机电路

• 发表时间: 2022
• 期刊: 2022 25th International Symposium on Design and Diagnostics of Electronic Circuits & Systems (DDECS
• 作者: Hoffend, Owen;Hayes, John P.
• 通讯作者: Hayes, John P.