SPX: Collaborative Research: Scalable Neural Network Paradigms to Address Variability in Emerging Device based Platforms for Large Scale Neuromorphic Computing

未来的计算机数据中心正被大量工作负载淹没，需要使用耗电的深度神经网络(DNN)模型进行高级别计算。基于使用新存储设备构建的内存中处理的DNN加速器可以为数据中心提供极高的能效和性能。这些加速器面临的一个挑战是它们的稳定性很差。这是由于新存储设备的物理限制。这个项目旨在通过开发有效的神经网络方法来解决这个问题。拟议研究的一个影响是开发更强大、可伸缩和可持续的深度学习计算系统。这将带来新的消费、商业、科学和国家安全应用。它将影响大数据和云计算领域。这个项目将在计算机工程和渴望深度学习能力的领域取得新的成果。它将使学生接触到尖端知识和动手研究的机会，并提高他们的能力。这将增强他们面对当今竞争激烈的全球就业市场的信心。教育影响包括课程整合、研究成果和外展活动。这项研究的目标是解决现有的基于记忆中处理的神经网络加速器中的一个关键问题，即由于器件特性引起的重量不确定而导致的稳定性较差。为了以可扩展和可持续的方式为未来的数据中心提升这些有前途的新兴加速器的稳定性，该项目将包括四项任务：1)明确地将权重不确定性建模，其可能表现出从设备的非理想状态提取的空间相关性，作为参数化的正则分布。2)一种统计神经网络结构，它可以很容易地集成到现有的卷积神经网络结构中，通过将其确定性操作替换为对参数化正则分布的统计对应操作。3)受纠错输出编码和现代神经网络体系结构的启发，提出了一种变异性感知神经网络分类器。4)不接触神经网络的可变性感知输入预处理。这些范例将适用于不同的软件和硬件平台，并将通过包括图像分类、生物医学图像分割和无人机目标跟踪在内的广泛现实应用程序来实施和评估。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Future computer data centers are being flooded with workloads requiring high-levels of computation using power-hungry deep neural network (DNN) models. DNN accelerators based on processing in memory built with new storage devices can offer great energy efficiency and performance for data centers. One challenge faced by these accelerators is their poor stability. This is due to the physical limitations of the new storage devices. This project aims to address this issue by developing efficient approaches to neural networks. One impact of proposed research is to develop more powerful, scalable, and sustainable deep learning computing systems. This will result in new consumer, business, scientific and national security applications. It will affect the fields of big data and cloud computing. This project will lead to new results in Computer Engineering and in fields that are hungry for deep learning capabilities. It will expose students to cutting-edge knowledge and hands-on research opportunities and elevate their competence. It will increase their confidence in facing today's highly competitive global job market. The education impact includes course integration of research results and outreach activities. Special attention is given in this to including women and underrepresented minority groups.The goal of the proposed research is to address a key issue in existing processing-in-memory-based neural network accelerators built with emerging nonvolatile devices, which is the bad stability due to weight uncertainties induced by the device characteristics. To escalate the stability of these promising emerging accelerators in a scalable and sustainable manner for future data centers, the project will include four tasks: 1) the explicitly modeling of weight uncertainties, which may exhibit spatial correlations extracted from device non-idealities, as parameterized canonical distributions. 2) a statistical neural network paradigm, which can be easily integrated into existing convolutional neural network architectures by replacing their deterministic operations with the statistical counterparts operating on parameterized canonical distributions. 3) variability-aware neural network classifier inspired by error correction output codes and modern neural network architecture. 4) variability-aware input pre-processing without touching neural networks. These paradigms will be generic to different software and hardware platforms, and will be implemented and evaluated with a wide set of real-world applications including image classification, biomedical image segmentation, and drone target tracking.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.