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FET: Small: Collaborative Research: A Probability Correlator for All-Magnetic Probabilistic Computing: Theory and Experiment

FET: Small: Collaborative Research: A Probability Correlator for All-Magnetic Probabilistic Computing: Theory and Experiment
FET:小型:协作研究:全磁概率计算的概率相关器:理论与实验
批准号:
2006843
负责人:
Supriyo Bandyopadhyay
金额:
$25.0万
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
项目状态:
已结题
起止时间:
2020-06-01 至 2024-05-31

项目摘要

项目成果

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中文摘要
翻译
概率计算是一种计算范式,可以比传统的数字计算更有效地解决某些问题。虽然数字计算处理的是确定性的二进制位,要么是0,要么是1,但概率计算处理的是概率位(p位),有时是0,有时是1。这与处理量子比特(q比特)的量子计算不同,量子比特是0和1的叠加(因此始终是0和1的混合)。量子计算通常需要最多的硬件资源,而数字计算需要最少的硬件资源,概率计算介于两者之间。概率计算中的大多数硬件资源都致力于生成两个或多个p比特流之间的特定相关性。这个项目将研究和演示一个系统,它将大大减少与生成相关性相关的硬件负担。这些结果将使概率计算比目前更有效。该项目将教育K-12,本科生和研究生在这一领域,以增加有技能的科学家和工程师的人才库,同时推进计算领域。概率计算的主要挑战之一是需要复杂的硬件来生成所需的概率比特流之间的相关性。这种硬件通常由微控制器、模数转换器、移位寄存器等组成,这会消耗大量的功率,并极大地扩展了系统在芯片上的占地面积。在这个项目中,一个超紧凑和非常节能的相关器或反相关器将被研究和证明,可以在两个p比特流之间产生可调程度的反相关。该方法是用两个磁性隧道结(MTJ),其软层是在接近,因此偶极耦合。位状态被编码在MTJ的电阻状态(高或低)中。一个MTJ在由递送自旋转移扭矩的自旋极化电流驱动时生成p位。电流以由其幅度确定的概率将电阻状态设置为高或低,而另一个MTJ的位状态由与第一个MTJ的偶极耦合确定。非常强的偶极耦合将导致完美的反相关,而非常弱的偶极耦合将导致没有相关。偶极耦合的效应将通过向第二MTJ施加(电生成的)局部应变来控制,这调制其内部能量势垒,从而将p位之间的反相关度从0%调制到100%。该项目将导致新的理解的设备,使用新兴的纳米磁物理学的下一代计算。这个奖项反映了NSF的法定使命,并已被认为是值得的支持,通过评估使用基金会的智力价值和更广泛的影响审查标准。
英文摘要
Probabilistic computing is a computing paradigm that can solve certain problems more efficiently than traditional digital computing. While digital computing deals with deterministic binary bits that are either 0 or 1, probabilistic computing deals with probabilistic bits (p- bits) that are sometimes 0 and sometimes 1. This is distinct from quantum computing that deals with quantum bits (q-bits) which are a superposition of 0 and 1 (and hence a mixture of both 0 and 1 all the time). Quantum computing usually requires the most hardware resources and digital computing the least, with probabilistic computing between the two. Most of the hardware resources in probabilistic computing are devoted to generating specific correlations between two or more p-bit streams. This project will study and demonstrate a system that will greatly reduce the hardware burden associated with generating correlations. The results will make probabilistic computing much more efficient than it currently is. The project will educate K-12, undergraduate, and graduate students in this field to increase the pool of skilled scientists and engineers while advancing the field of computing.One of the major challenges in probabilistic computing is the complex hardware needed to generate required correlations between probabilistic bit streams. This hardware usually consists of microcontrollers, analog-to-digital converters, shift registers, etc., that consume significant power and vastly expand the system’s footprint on a chip. In this project, an ultra-compact and extremely energy-efficient correlator or anti-correlator will be studied and demonstrated that can generate tunable degrees of anti-correlation between two p-bit streams. The approach is implemented with two magnetic tunnel junctions (MTJs) whose soft layers are in close proximity and hence dipole-coupled. Bit states are encoded in the resistance states (high or low) of the MTJs. One MTJ generates a p-bit when driven by a spin-polarized current delivering a spin-transfer-torque. The current sets the resistance state high or low with a probability determined by its magnitude, while the bit state of the other MTJ is determined by dipole coupling with the first. Very strong dipole coupling will result in perfect anti-correlation, while very weak dipole coupling will result in no correlation. The effect of dipole coupling will be controlled by applying (electrically generated) local strain to the second MTJ, which modulates its internal energy barrier, thereby modulating the degree of anti-correlation between the p-bits from 0% to 100%. This project will result in new understanding of devices that use emerging nanomagnetic physics for the next generation of 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.
期刊论文(5)
专著(0)
科研奖励(0)
会议论文
DOI: 10.1039/d1nr01177d
发表时间: 2021-05-14
期刊: NANOSCALE
影响因子: 6.7
作者: [De, Anulekha, Drobitch, Justine Lynn, Barman, Anjan]
通讯作者: Barman, Anjan
DOI: 10.1088/1361-6528/ac2f59
发表时间: 2021-10
期刊: Nanotechnology
影响因子: 3.5
作者: [B. Rana;Amrita Mondal;Supriyo Bandyopadhyay;A. Barman]
通讯作者: B. Rana;Amrita Mondal;Supriyo Bandyopadhyay;A. Barman
DOI: 10.1109/access.2021.3049333
发表时间: 2021-01
期刊: IEEE Access
影响因子: 3.9
作者: [Supriyo Bandyopadhyay]
通讯作者: Supriyo Bandyopadhyay
EAGER: Spintronic extreme sub-wavelength and super-gain active electronically scanned antenna (AESA) enabled by phonon-magnon-plasmon-photon coupling.
  • 批准号:
    2235789
  • 项目类别:
    Standard Grant
  • 资助金额:
    $22.0万
  • 财政年份:
    2022
  • 负责人:
    Supriyo Bandyopadhyay
  • 依托单位:
EAGER: Collaborative Research: Bayesian Reasoning Machine on a Magneto-Tunneling Junction Network
  • 批准号:
    2001255
  • 项目类别:
    Standard Grant
  • 资助金额:
    $10.0万
  • 财政年份:
    2020
  • 负责人:
    Supriyo Bandyopadhyay
  • 依托单位:
Single nanowire spin-valve based infrared photodetctors and equality bit comparators
  • 批准号:
    1609303
  • 项目类别:
    Standard Grant
  • 资助金额:
    $37.5万
  • 财政年份:
    2016
  • 负责人:
    Supriyo Bandyopadhyay
  • 依托单位:
NEB: Hybrid Spintronics and Straintronics: A New Technology for Ultra-Low Energy Computing and Signal Processing Beyond the Year 2020.
  • 批准号:
    1124714
  • 项目类别:
    Standard Grant
  • 资助金额:
    $155.0万
  • 财政年份:
    2011
  • 负责人:
    Supriyo Bandyopadhyay
  • 依托单位:
国内基金
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  • 批准号:
  • 项目类别:
    省市级项目
  • 资助金额:
    --
  • 批准年份:
    2024
  • 负责人:
  • 依托单位:
tRNA-derived small RNA上调YBX1/CCL5通路参与硼替佐米诱导慢性疼痛的机制研究
  • 批准号:
  • 项目类别:
    省市级项目
  • 资助金额:
    10.0万元
  • 批准年份:
    2022
  • 负责人:
    张祥忠
  • 依托单位:
Small RNA调控I-F型CRISPR-Cas适应性免疫性的应答及分子机制
Small RNAs调控解淀粉芽胞杆菌FZB42生防功能的机制研究
  • 批准号:
    31972324
  • 项目类别:
    面上项目
  • 资助金额:
    58.0万元
  • 批准年份:
    2019
  • 负责人:
    高学文
  • 依托单位: