Collaborative Research: FET: Medium: Probabilistic Computing Through Integrated Nano-devices - A Device to Systems Approach

合作研究：FET：中：通过集成纳米设备进行概率计算 - 设备到系统的方法

• 批准号: 2106260
• 负责人: Kerem Camsari
• 金额: $ 27万
• 依托单位: University of California-Santa Barbara
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2106260&HistoricalAwards=false
• 关键词: Collaborative; Research; FET; Medium; Probabilistic

The world has seen a steady increase in demand for computational power, with no end in sight. Search engines that allow users to find answers to pretty much all of their questions are expected to deliver results in less than a second, while understanding the customer with a high probability based on just a few words of input. This is not the type of computation that is precise and gives the “correct” answer; it instead provides solutions that are more associative, mimicking the human way of addressing problems. In fact, many problems in the real world are probabilistic in nature, and conventional computing schemes are not optimized for these tasks. In 1982 Nobel Prize winner Richard Feynman stated in recognition of this fact: “The way to simulate a probabilistic nature might still be by a computer which itself is probabilistic... So, it becomes what I’ll call a probabilistic computer, in which the output is not a unique function of the input.” To promote the progress of science, this proposal aims to explore, model and build hardware components and circuits that ultimately enable such a probabilistic computer, which will greatly benefit the society as a whole. Moreover, educational tools, new courses and training opportunities both for undergraduate and graduate students are being created that expose them to a device-to-systems research program on probabilistic computing in order to prepare them for the new era of electronics.The project adopts a Device-to-Systems approach that covers experiments from single devices and small circuits all the way up to simulations with thousands of devices. It addresses the question of how to implement probabilistic functionality in hardware from a variety of different angles. The key objective of this proposal is to take the next step in the development of probabilistic computing by experimental demonstrations of integrated probabilistic bits (p-bits) and p-circuits and quantifying advantages of scaled probabilistic computers through key figures-of-merits in system-level applications based on experimental input. This is being achieved by employing unstable magnets in a magnetic tunneling junction (MTJ) configuration as random number generators that become tunable by the use of field-effect transistors in a suitable circuit layout. Initial projections estimate that if integrated MTJs coupled with conventional transistors can be scaled up, one can expect to achieve orders of magnitude improvements compared to what is achievable in conventional semiconductor technology in key figures-of-merits, such as the number of statistically independent samples per second (also referred to as flips per second) that a probabilistic sampler can go through.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.

世界对计算能力的需求稳步增长，看不到尽头。搜索引擎允许用户找到几乎所有问题的答案，预计将在不到一秒的时间内提供结果，同时仅基于几个字的输入就很有可能理解客户。这不是精确的计算类型，并给出“正确”的答案;相反，它提供了更具关联性的解决方案，模仿人类解决问题的方式。事实上，真实的世界中的许多问题在本质上是概率性的，并且常规计算方案没有针对这些任务进行优化。1982年，诺贝尔奖赢家理查德·费曼（Richard Feynman）承认了这一事实：“模拟概率性质的方法仍然可能是通过一台本身就是概率的计算机。所以，它变成了我所说的概率计算机，其中输出不是输入的唯一函数。为了促进科学的进步，该提案旨在探索、建模和构建最终实现这种概率计算机的硬件组件和电路，这将极大地造福于整个社会。此外，还为本科生和研究生提供了教育工具、新课程和培训机会，使他们能够接触到概率计算的设备到系统研究计划，为电子学的新时代做好准备。该项目采用设备到系统的方法，涵盖从单个设备和小型电路到数千个设备的模拟实验。它从各种不同的角度解决了如何在硬件中实现概率功能的问题。该提案的主要目标是通过集成概率位（p位）和p电路的实验演示，并通过基于实验输入的系统级应用中的关键指标来量化缩放概率计算机的优势，从而在概率计算的发展中迈出下一步。这是通过在磁隧道结（MTJ）配置中使用不稳定的磁体作为随机数发生器来实现的，该随机数发生器通过在合适的电路布局中使用场效应晶体管而变得可调。初步预测估计，如果与传统晶体管耦合的集成MTJ可以按比例放大，则与传统半导体技术中可实现的相比，可以预期在关键品质因数方面实现数量级的改进，例如每秒统计独立样本的数量（也称为每秒翻转次数）该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的学术价值和更广泛的影响审查标准。

项目成果

Massively parallel probabilistic computing with sparse Ising machines

• DOI: 10.1038/s41928-022-00774-2
• 发表时间: 2022-06-02
• 期刊: NATURE ELECTRONICS
• 影响因子: 34.3
• 作者: Aadit, Navid Anjum;Grimaldi, Andrea;Camsari, Kerem Y.
• 通讯作者: Camsari, Kerem Y.

Efficient Probabilistic Computing with Stochastic Perovskite Nickelates

• DOI: 10.1021/acs.nanolett.2c03223
• 发表时间: 2022-10-31
• 期刊: NANO LETTERS
• 影响因子: 10.8
• 作者: Park, Tae Joon;Selcuk, Kemal;Ramanathan, Shriram
• 通讯作者: Ramanathan, Shriram

A Full-Stack View of Probabilistic Computing With p-Bits: Devices, Architectures, and Algorithms

• DOI: 10.1109/jxcdc.2023.3256981
• 发表时间: 2023-02
• 期刊: IEEE Journal on Exploratory Solid-State Computational Devices and Circuits
• 影响因子: 2.4
• 作者: S. Chowdhury;Andrea Grimaldi;Navid Anjum Aadit;Shaila Niazi;M. Mohseni;S. Kanai;Hideo Ohno;S. F