Collaborative Research: FMitF: Track-1: Correctness at Both Ends: Rigorous ML Meets Efficient Sparse Implementations

协作研究：FMitF：Track-1：两端的正确性：严格的 ML 满足高效的稀疏实现

基本信息

批准号：
2124205
负责人：
John Baugh
金额：
$ 30万
依托单位：
North Carolina State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-10-01 至 2024-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2124205&HistoricalAwards=false
关键词：
Collaborative Research FMitF Track Correctness

项目摘要

This project called CBE (correctness at both ends) addresses the growing concern that on one hand systems based on deep neural networks (DNNs) are playing an increasing role in critical applications, but on the other hand these systems can do significant damage if they harbor software defects. These defects go beyond the familiar logic bugs, including also semantic bugs such as misclassifying medical images. Traditionally, attempts to make DNNs more efficient by sparsifying them have often resulted in increased levels of semantic bugs. The project's novelties are its integrated approach to sparsify networks while preserving semantic correctness as well as helping eliminate logic bugs. The project's impacts are in making DNNs energy-efficient, permitting their deployment in edge devices, while also helping eliminate their defects.CBE employs a knowledge-distillation paradigm wherein a sparsified network is trained by imitating the parent network's classification behavior. Sparsification steps that meet higher level semantic objectives may unfortunately lead to an inefficient sparse implementation -- especially in newly introduced GPUs for which hand-tuned sparse libraries are unavailable. The CBE project supports the developers of such libraries by also providing low-level implementation verification tools. The investigators are domain experts in DNN semantics and optimization, and also in softwareverification. Their three-year collaborative research project is taking case studies of DNNs from critical areas such as medical imaging, and showing how DNNs can be sparsifed to ensure correctness at both ends. The main impact of this project is to develop prototype software tools that can help spur further research and technology development. Another major impact is the training of students who will fill critical roles in the fast-growing area of deployable machine-learning systems where talent shortage can cripple the nation's economy and security.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.

这个称为CBE（两端的正确性）的项目越来越担心，即基于深层神经网络（DNNS）的一个手系统在关键应用程序中起着越来越多的作用，但另一方面，如果它们具有软件缺陷，则这些系统会造成重大损害。这些缺陷超出了熟悉的逻辑错误，包括语义错误，例如错误分类医学图像。传统上，试图通过稀疏来提高DNN的尝试通常会导致语义错误的水平增加。该项目的新颖性是其集成的方法来稀疏网络，同时保持语义正确性以及帮助消除逻辑错误。该项目的影响在于使DNNS节能，允许其在边缘设备中部署，同时也有助于消除其缺陷。不幸的是，符合更高级别语义目标的稀疏步骤可能导致稀疏实施效率低下 - 尤其是在新引入的GPU中，无法使用手工调整的稀疏库。 CBE项目还通过提供低级实施验证工具来支持此类库的开发人员。研究人员是DNN语义和优化以及软件验证方面的领域专家。他们为期三年的协作研究项目正在对医学成像等关键领域的DNN进行案例研究，并展示了如何对DNN进行稀疏以确保两端的正确性。该项目的主要影响是开发原型软件工具，可以帮助刺激进一步的研究和技术开发。另一个主要影响是培训学生，他们将在可部署的机器学习系统的快速增长领域中填补关键作用，在该系统中，人才短缺可能会削弱国家的经济和安全性。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的审查标准来通过评估来获得支持的。