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

基本信息

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

项目摘要

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，技术进步不断创建的数据比我们所能应付的更多数据。大部分数据处理将至少在Internet边缘的设备（例如传感器和智能手机）的设备上运行。但是，在这些设备上运行现有的机器学习算法将耗尽电池电池，并且也太慢了。高维（HD）计算是一类学习算法，它是由于人脑在许多简单数据并行运行的观察过程中所激发的。与当今的深神经网络和其他类似的算法相反，使用高清计算来学习的系统将能够高效运行至少千倍，可以直接在非挥发性内存中实现，并且在使用的情况下更安全，因为它们使用使用大量位（〜10,000）并行编码和处理数据。最重要的是，这样的系统可以解释它们如何做出决策，从而导致传感器和手机可以直接从获得的数据中学习，而无需对电池寿命的最小影响云的影响。该项目将开发高清计算软件和硬件基础架构，以便工程师可以轻松地在其产品中提供高清计算功能，从而从其速度和能源效率中受益。该项目将支持代表性不足的少数族裔学生，包括K-12外展活动，并通过开源努力传播其成果和代码。该项目试图发展：i）支持高维空间中关键认知计算的新颖算法，包括分类，聚类和聚类和聚类，集群，集群和回归； ii）在传感器和移动设备上进行有效高清计算的新型系统，这些系统涵盖了内存中的硬件加速器，例如GPU，FPGA和处理器（PIM），以及软件基础架构以支持它。原型将在智能房屋中建造和测试，并在一个名为HPWREN的大型传感器网络中进行，用于许多应用程序，包括消防，覆盖了圣地亚哥地区20,000平方英里。这些演示将既显示拟议的HD算法的质量，又显示系统设计的效率来解决现实世界的学习问题。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛的影响来获得支持的。审查标准。

项目成果

期刊论文数量（3）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

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
影响因子：
0
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