FET: Small: Brain-Inspired Hyperdimensional Computing for IoT Applications

FET：小型：用于物联网应用的类脑超维计算

• 批准号: 1911095
• 负责人: Tajana Rosing
• 金额: $ 50万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-01 至 2023-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1911095&HistoricalAwards=false
• 关键词: FET; Small; Brain; Inspired; Hyperdimensional

In today's world, technological advances are continually creating more data than what we can cope with. Much of data processing will need to run at least partly on devices at the edge of the internet, such as sensors and smart phones. However, running existing machine learning algorithms on these devices would drain their batteries and be also too slow. Hyper-Dimensional (HD) computing is a class of learning algorithms that is motivated by the observation that the human brain operates on a lot of simple data in parallel. In contrast to today's Deep Neural Networks and other similar algorithms, systems that use HD computing to learn will be able to run at least thousand times more efficiently, can be implemented directly in non-volatile memory, and are natively more secure as they use a large number of bits (~10,000) to encode and process data in parallel. Most importantly, such systems can explain how they made decisions, resulting in sensors and phones that can learn directly from the data they obtain without the need for the cloud at minimum impact to their battery lifetime. This project will develop HD computing software and hardware infrastructure, so that engineers can easily provide HD computing capabilities in their products, and thus benefit from their speed and energy efficiency. The project will support underrepresented minority students including K-12 outreach activities, and disseminate its outcomes and code through open-source efforts.The project seeks to develop: i) novel algorithms supporting key cognitive computations in high-dimensional space including classification, clustering and regression; and ii) novel systems for efficient HD computing on sensors and mobile devices, which cover hardware accelerators such as GPUs, FPGAs and Processor in Memory (PIM), along with software infrastructure to support it. Prototypes will be built and tested in smart homes, and in a large scale sensor network called HPWREN, used for many applications including firefighting, covering 20,000 sq. miles in San Diego area. These demonstrations will show both the quality of the proposed HD algorithms and the efficiency of system designs to address the real-world learning problems.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.

在当今世界，技术进步不断创造更多的数据比我们可以科普。大部分数据处理将需要至少部分在互联网边缘的设备上运行，如传感器和智能手机。然而，在这些设备上运行现有的机器学习算法会耗尽它们的电池，而且速度太慢。 超维（HD）计算是一类学习算法，其动机是观察到人类大脑并行处理大量简单数据。 与今天的深度神经网络和其他类似算法相比，使用HD计算进行学习的系统将能够以至少千倍的效率运行，可以直接在非易失性存储器中实现，并且由于它们使用大量比特（约10，000）来并行编码和处理数据，因此天生更安全。最重要的是，这样的系统可以解释他们是如何做出决策的，从而使传感器和手机可以直接从他们获得的数据中学习，而不需要云计算，对电池寿命的影响最小。该项目将开发高清计算软件和硬件基础设施，以便工程师可以轻松地在其产品中提供高清计算功能，从而受益于其速度和能效。 该项目将支持代表性不足的少数民族学生，包括K-12外展活动，并通过开源努力传播其成果和代码。该项目旨在开发：i）支持高维空间中关键认知计算的新算法，包括分类，聚类和回归;以及ii）用于在传感器和移动的设备上进行高效HD计算的新颖系统，其覆盖诸如GPU、FPGA和存储器中处理器（PIM）的硬件加速器，沿着而来的是软件基础设施的支持。原型将在智能家居和一个名为HPWREN的大规模传感器网络中进行构建和测试，该网络用于包括消防在内的许多应用，覆盖20，000平方米。在圣地亚哥地区。 这些演示将展示拟议的HD算法的质量和系统设计的效率，以解决现实世界的学习问题。这个奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

项目成果

QuantHD: A Quantization Framework for Hyperdimensional Computing

• DOI: 10.1109/tcad.2019.2954472
• 发表时间: 2020-10-01
• 期刊: IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS
• 影响因子: 2.9
• 作者: Imani, Mohsen;Bosch, Samuel;Rosing, Tajana
• 通讯作者: Rosing, Tajana

Efficient Associative Search in Brain-Inspired Hyperdimensional Computing

类脑超维计算中的高效关联搜索

• DOI: 10.1109/mdat.2019.2919954
• 发表时间: 2019
• 期刊: IEEE design test of computers
• 作者: Imani, M;Morris, J;Shu, H;Rosing, T
• 通讯作者: Rosing, T

SearcHD: A Memory-Centric Hyperdimensional Computing With Stochastic Training

• DOI: 10.1109/tcad.2019.2952544
• 发表时间: 2020-10-01
• 期刊: IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS
• 影响因子: 2.9
• 作者: Imani, Mohsen;Yin, Xunzhao;Rosing, Tajana
• 通讯作者: Rosing, Tajana