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
• 负责人: Kerem Camsari
• 金额: $ 79.95万
• 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: 2023-07-01 至 2027-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2311295&HistoricalAwards=false
• 关键词: Collaborative; Research; SHF; Medium; Verifying

This project addresses the critical need for verifying the safety and reliability of deep neural networks (DNNs) used in various applications, such as autonomous driving, aircraft control, and consumer products like smartphones. As the demand for computational power in artificial intelligence continues to grow, this project explores innovative domain-specific architectures (DSAs) such as quantum and probabilistic computing platforms as potential solutions to the verification problem. The research's significance lies in ensuring the correct functioning of DNNs when exposed to perturbations or attacks, with the goal of benefiting society by enhancing the safety and trustworthiness of Artificial Intelligence (AI)-driven technologies. This interdisciplinary project will not only advance the field of DNN verification and energy-efficient domain-specific computers but also support education and workforce development, increasing diversity and collaboration between academia and industry through targeted activities.The project aims to solve the exact DNN verification problem using quantum annealing and probabilistic computers, contrasting conventional classical computing approaches. The research contributions span device, circuit, system, and algorithm levels. At the device level, the project will improve the energy efficiency of existing probabilistic-bit (p-bit) designs by leveraging the voltage-controlled-magnetic anisotropy (VCMA) phenomenon. At the circuit and architecture level, the team will design array-level spintronic p-computers (i.e., computers powered by p-bits) and investigate the dynamics between the feedback circuitry and p-bits, complemented by scaled complementary metal-oxide semiconductor (CMOS) emulators (Field Programmable Gate Arrays - FPGAs) with more than 1000 p-bits. At the algorithm level, the project will focus on formulating the exact verification of a neural network as an Ising model problem, which will be solved using the developed hybrid classical/probabilistic computers. The project will create pathways for large-scale spintronic probabilistic computers and explore new research directions, such as applying the simulated quantum annealing algorithm for DNN verification. This work will lay the foundations for p-computers with more than one million p-bits, enabled by Magnetic Random Access Memory (MRAM) technology with far-reaching applications beyond verification, including machine learning and quantum simulation.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.

该项目解决了验证在各种应用程序中使用的深神经网络（DNN）的安全性和可靠性的关键需求，例如自动驾驶，飞机控制和智能手机等消费产品。随着人工智能中对计算能力的需求不断增长，该项目探索了创新的领域特定体系结构（DSA），例如量子和概率计算平台，作为验证问题的潜在解决方案。这项研究的意义在于确保在暴露于扰动或攻击时DNN的正确功能，其目的是通过增强人工智能（AI）驱动技术的安全性和可信赖性来使社会受益。这个跨学科的项目不仅将推进DNN验证和节能领域特异性计算机的领域，还可以通过目标活动来支持教育和劳动力发展，从而增加学术界和行业之间的多样性和协作。该项目的目的是解决精确的DNN验证问题，使用量子退火和概率计算机来解决准确的DNN验证问题，并将其构成构成典型的计算机计算方法。研究贡献涵盖了设备，电路，系统和算法水平。在设备级别，该项目将通过利用电压控制的磁性各向异性（VCMA）现象来提高现有概率位（P-BIT）设计的能效。在电路和架构层面，团队将设计阵列级别的Spintronic P-Computer（即由P-BITS提供动力的计算机），并研究反馈电路和P-BITS之间的动态，并补充了缩放互补的金属氧化物半导体（CMOS）模拟器（CMOS）模拟器（现场可编程的网格阵列），并与更多的1000 piTs相比。在算法级别上，该项目将集中于制定神经网络作为ISING模型问题的确切验证，该验证将使用开发的混合经典/概率计算机来解决。该项目将为大规模旋转概率计算机创建途径，并探索新的研究方向，例如将模拟量子退火算法应用于DNN验证。这项工作将为具有超过一百万个P位的P计算机奠定基础，这是由磁性随机存储记忆（MRAM）技术实现的，具有深远的应用程序，包括机器学习和量子仿真，包括NSF的法定任务，并被认为是值得通过基金会的智力功能和广泛影响的评估来审查CRETIRIA的法定任务。