CAREER: Physics-inspired Machine Learning with Sparse and Asynchronous p-bits

职业:利用稀疏和异步 p 位进行物理启发的机器学习

基本信息

  • 批准号:
    2237357
  • 负责人:
  • 金额:
    $ 54.61万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Continuing Grant
  • 财政年份:
    2023
  • 资助国家:
    美国
  • 起止时间:
    2023-01-01 至 2027-12-31
  • 项目状态:
    未结题

项目摘要

Recent advances in the fields of Machine Learning and Artificial Intelligence (AI) have created practical applications ranging from powerful chatbots that can generate meaningful conversations or AI artists that can generate striking art. Behind the stage, however, there are enormous costs in energy, time and physical resources to train such AI models, making them costly, limiting accessibility and preventing democratized use. Moreover, these revolutionary advances have come at the worst possible time from a microelectronics viewpoint, since it has become significantly hard to improve the energy efficiency and performance of modern transistors whose dimensions have reached atomic dimensions. This project is about designing a new kind of physics-inspired and probabilistic computer, contrasting conventional fully-deterministic computers. The approach is to start from inherently noisy magnetic materials and devices to build probabilistic bits (p-bits). Networks of connected p-bits can then be suitably configured to efficiently solve computational problems encountered in probabilistic machine learning containing a large family of powerful algorithms that are hard to train in conventional computers. Because the underlying building blocks are naturally probabilistic in this approach, they can be used to implement probabilistic learning algorithms far more efficiently compared to conventional computers, where mimicking true randomness comes with high costs in area and energy consumption. The interdisciplinary nature of this project will require the synergy and rethinking of several different layers of the computing stack from devices, architectures and algorithms such that new types of energy-efficient, physics-inspired and probabilistic computers can be built to help with the greatest computing challenges of society.The specific approach of this CAREER project is to design physics-inspired probabilistic computers (p-computer) tailored for probabilistic machine learning algorithms. These p-computers will go beyond existing small-scale prototypes by combining magnetic nanodevices called stochastic Magnetic Tunnel Junctions with powerful CMOS-based field programmable gate arrays. The main aim will be to demonstrate the first large-scale demonstration of a CMOS + stochastic MTJ architecture for probabilistic computing where 10,000 digital p-bits will be augmented by 100 stochastic magnetic tunnel junction-based p-bits. Augmented by the true randomness and the asynchronous dynamics naturally provided by stochastic magnetic tunnel junctions, these heterogeneous processors are expected to provide orders of magnitude energy and performance improvement over optimized Graphical and Tensor Processing Units commonly used by present-day AI systems. The application of these p-computers to quantum and classical machine learning algorithms in physics-inspired, hardware-aware and sparse networks will lead to computational advantage and better energy efficiency, facilitating the eventual integration of million p-bit computers. The findings of this project will lead to the development of unique device models and algorithms, interdisciplinary courses and tutorials. These will be disseminated on nanoHUB and YouTube covering a diverse array of topics, including statistical mechanics, machine learning and quantum computing. Through partnerships with supporting institutions in academia and industry, this project will strongly contribute to the workforce training of the “technology maestros” of the future who are deep in one field but broad enough to connect to related areas.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.
机器学习和人工智能(AI)领域的最新进展创造了实际应用,从可以生成有意义对话的强大聊天机器人到可以创作引人注目的艺术的人工智能艺术家。然而,在舞台背后,训练此类人工智能模型需要花费巨大的精力、时间和物理资源,使其成本高昂,限制了可访问性并阻碍了民主化使用。此外,从微电子学的角度来看,这些革命性的进步是在最糟糕的时候出现的,因为现代晶体管的尺寸已经达到原子尺寸,要提高其能效和性能已经变得非常困难。这个项目是关于设计一种新的物理启发和概率计算机,对比传统的完全确定性计算机。该方法是从固有噪音的磁性材料和设备开始构建概率比特(p比特)。然后,连接的p位网络可以被适当地配置为有效地解决概率机器学习中遇到的计算问题,该概率机器学习包含一大系列难以在传统计算机中训练的强大算法。因为在这种方法中,底层的构建块自然是概率性的,所以与传统计算机相比,它们可以更有效地实现概率学习算法,在传统计算机中,模仿真正的随机性会带来很高的面积和能耗成本。该项目的跨学科性质将需要协同作用和重新思考计算堆栈的几个不同层,包括设备,架构和算法,从而实现新型的节能,物理学启发的概率计算机可以用来帮助解决社会中最大的计算挑战。这个CAREER项目的具体方法是设计物理学启发的概率计算机(p-computer)为概率机器学习算法量身定制。这些p计算机将超越现有的小规模原型,将称为随机磁性隧道结的磁性纳米器件与强大的基于CMOS的现场可编程门阵列相结合。主要目的是展示用于概率计算的CMOS +随机MTJ架构的首次大规模演示,其中10,000个数字p位将由100个基于随机磁性隧道结的p位增强。通过随机磁性隧道结自然提供的真实随机性和异步动态增强,这些异构处理器有望提供比当前AI系统常用的优化图形和张量处理单元更高的能量和性能。在物理启发的、硬件感知的和稀疏的网络中,这些p-计算机在量子和经典机器学习算法中的应用将带来计算优势和更好的能源效率,促进百万p-比特计算机的最终集成。该项目的研究结果将导致开发独特的设备模型和算法,跨学科课程和教程。这些内容将在nanoHUB和YouTube上传播,涵盖各种主题,包括统计力学、机器学习和量子计算。通过与学术界和工业界的支持机构合作,该项目将为培养未来的“技术大师”做出巨大贡献,这些人在一个领域有很深的造诣,但又足够广泛,可以连接到相关领域。该奖项反映了NSF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

期刊论文数量(1)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Machine Learning Quantum Systems with Magnetic p-bits
具有磁性 p 位的机器学习量子系统
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Kerem Camsari其他文献

Kerem Camsari的其他文献

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{{ truncateString('Kerem Camsari', 18)}}的其他基金

Collaborative Research: SHF: Medium: Verifying Deep Neural Networks with Spintronic Probabilistic Computers
合作研究:SHF:中:使用自旋电子概率计算机验证深度神经网络
  • 批准号:
    2311295
  • 财政年份:
    2023
  • 资助金额:
    $ 54.61万
  • 项目类别:
    Continuing Grant
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
  • 财政年份:
    2021
  • 资助金额:
    $ 54.61万
  • 项目类别:
    Continuing Grant

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  • 批准号:
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  • 批准年份:
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  • 项目类别:
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    24.0 万元
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    2012
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Chinese physics B
  • 批准号:
    11024806
  • 批准年份:
    2010
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  • 财政年份:
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