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

SPX：协作研究：可扩展神经网络范式，以解决基于新兴设备的大规模神经形态计算平台的可变性

• 批准号: 2006748
• 负责人: Wujie Wen
• 金额: $ 35.55万
• 依托单位: Lehigh University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-05 至 2024-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2006748&HistoricalAwards=false
• 关键词: SPX; Collaborative; Research; Scalable; Neural

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.

未来的计算机数据中心将充斥着需要使用耗电的深度神经网络 (DNN) 模型进行高级计算的工作负载。基于新型存储设备构建的内存处理的 DNN 加速器可以为数据中心提供出色的能源效率和性能。这些加速器面临的挑战之一是稳定性差。这是由于新存储设备的物理限制。该项目旨在通过开发有效的神经网络方法来解决这个问题。拟议研究的影响之一是开发更强大、可扩展和可持续的深度学习计算系统。这将带来新的消费者、商业、科学和国家安全应用。它将影响大数据和云计算领域。该项目将在计算机工程和渴望深度学习能力的领域带来新成果。它将让学生接触到前沿知识和实践研究机会，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。教育影响包括研究成果和推广活动的课程整合。其中特别关注包括女性和代表性不足的少数群体。拟议研究的目标是解决现有的基于内存处理的神经网络加速器使用新兴非易失性设备构建的一个关键问题，即稳定性差。权重由设备特性引起的不确定性。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）权重不确定性的显式建模，这可能会表现出从设备非理想性中提取的空间相关性，如参数化规范分布。 2）统计神经网络范式，通过用参数化规范分布上的统计对应操作替换其确定性操作，可以轻松地将其集成到现有的卷积神经网络架构中。 3）受纠错输出代码和现代神经网络架构启发的可变性感知神经网络分类器。 4）无需接触神经网络即可进行可变性感知输入预处理。这些范例将通用于不同的软件和硬件平台，并将在广泛的现实世界应用中实施和评估，包括图像分类、生物医学图像分割和无人机目标跟踪。该奖项反映了 NSF 的法定使命，并被视为值得通过使用基金会的智力优点和更广泛的影响审查标准进行评估来支持。

项目成果

NeuGuard: Lightweight Neuron-Guided Defense against Membership Inference Attacks

• DOI: 10.1145/3564625.3567986
• 发表时间: 2022-06
• 期刊: Proceedings of the 38th Annual Computer Security Applications Conference
• 作者: Nuo Xu;Binghui Wang;Ran Ran-Ran;Wujie Wen;P. Venkitasubramaniam

Spectral-DP: Differentially Private Deep Learning through Spectral Perturbation and Filtering

Spectral-DP：通过谱扰动和过滤实现差分隐私深度学习

• DOI: 10.1109/sp46215.2023.10179457
• 发表时间: 2023
• 期刊: 2023 the 44th IEEE Symposium on Security and Privacy (SP
• 作者: Feng, Ce;Xu, Nuo;Wen, Wujie;Venkitasubramaniam, Parv;Ding, Caiwen
• 通讯作者: Ding, Caiwen

A length adaptive algorithm-hardware co-design of transformer on FPGA through sparse attention and dynamic pipelining

• DOI: 10.1145/3489517.3530585
• 发表时间: 2022-07
• 期刊: Proceedings of the 59th ACM/IEEE Design Automation Conference
• 作者: Hongwu Peng;Shaoyi Huang;Shiyang Chen;Bingbing Li;Tong Geng;Ang Li;Weiwen Jiang;Wujie Wen;J. Bi;Hang Liu;Caiwen Ding

COLA: Orchestrating Error Coding and Learning for Robust Neural Network Inference Against Hardware Defects

• 发表时间: 2023
• 作者: Anlan Yu;Ning Lyu;Jieming Yin;Zhiyuan Yan;Wujie Wen

Improving Realistic Worst-Case Performance of NVCiM DNN Accelerators Through Training with Right-Censored Gaussian Noise

• DOI: 10.1109/iccad57390.2023.10323830
• 发表时间: 2023-07
• 期刊: 2023 IEEE/ACM International Conference on Computer Aided Design (ICCAD)
• 作者: Zheyu Yan;Yifan Qin;Wujie Wen;X. Hu;Yi Shi