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

• 批准号: 2237357
• 负责人: Kerem Camsari
• 金额: $ 54.61万
• 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: 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
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2237357&HistoricalAwards=false
• 关键词: CAREER; Physics; inspired; Machine; Learning

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）领域的最新进展已经创建了实用的应用程序，从强大的聊天机器人可以产生有意义的对话或可以产生罢工艺术的AI艺术家。然而，在舞台的后面，能源，时间和物理资源在训练此类AI模型，使其昂贵，限制可访问性并防止民主化使用。此外，从微电子的角度来看，这些革命性进步在最糟糕的时间到了，因为它很难提高尺寸达到原子维度的现代晶体管的能源效率和性能。该项目旨在设计一种新型的物理风格和概率的计算机，与传统上完全确定的计算机进行对比。该方法是从固有的噪声磁性材料和设备开始，以构建概率位（P-BITS）。然后，可以适当地配置连接的P-BITS网络，以有效地解决概率机器学习中遇到的计算问题，其中包含大型强大算法的家庭，这些算法很难在传统的计算机中训练。由于在这种方法中，基本的构建块自然是概率的，因此与传统计算机相比，它们可用于实施概率学习算法的效率要高得多，在该计算机中，模仿真正的随机性，面积和能源消耗的成本很高。该项目的跨学科性质将需要从设备，架构和算法的几个不同层的计算堆栈进行协同和重新思考，以便可以构建新型的节能，物理学和概率的计算机，以构建以实现这一最大的计算挑战。概率机器学习算法。这些P计算机将通过将称为随机磁性隧道连接的磁性纳米台与强大的基于CMOS的磁场可编程栅极阵列相结合，将超越现有的小规模原型。主要目的是证明CMOS +随机MTJ架构的首次大规模演示，用于概率计算，其中10,000个数字P-bits将通过基于100个随机磁性隧道连接处的P-BITS增强。这些异质处理器被随机磁性隧道连接自然提供的真实随机性和异步动力学增强，预计这些异质处理器将提供数量级的能量和性能改善，而不是当今AI系统常用的优化图形和张量处理单元。这些P计算机在物理启发，硬件感知和稀疏网络中的量子和经典机器学习算法的应用将带来计算优势和更好的能源效率，从而支持百万个P-PIT计算机的事件集成。该项目的发现将导致开发独特的设备模型和算法，跨学科课程和教程。这些将在纳米ub和YouTube上传播，其中包括统计力学，机器学习和量子计算在内的潜水员阵列。通过与学术界和行业的支持机构的伙伴关系，该项目将有力地为对未来的“技术大师”的劳动力培训做出贡献，这些培训在一个领域深处，但足够广泛，可以与相关领域建立联系。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子优点和广泛的影响来评估NSF的法定任务，并被认为是宝贵的支持。

项目成果

Machine Learning Quantum Systems with Magnetic p-bits

具有磁性 p 位的机器学习量子系统

• DOI: 10.1109/intermagshortpapers58606.2023.10228205
• 发表时间: 2023
• 期刊: 2023 IEEE International Magnetic Conference - Short Papers (INTERMAG Short Papers
• 作者: Chowdhury, Shuvro;Camsari, Kerem Y.
• 通讯作者: Camsari, Kerem Y.