Collaborative Research: SHF: Medium: Analog EDA-Inspired Methods for Efficient and Robust Neural Network Design

合作研究：SHF：媒介：用于高效、鲁棒神经网络设计的模拟 EDA 启发方法

基本信息

批准号：
2107321
负责人：
Zheng Zhang
金额：
$ 50.29万
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：
2021
资助国家：
美国
起止时间：
2021-07-15 至 2025-06-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2107321&HistoricalAwards=false
关键词：
Collaborative Research SHF Medium Analog

项目摘要

Deep neural networks have achieved great success in many engineering fields including, but not limited to, image classification, speech recognition, recommendation systems and autonomous driving. However, they suffer from two major challenges. Firstly, many neural network models are not robust, i.e. a neural network could produce inaccurate results when the input data experiences a very small amount of perturbation. Secondly, the huge cost of generating and deploying large-size neural networks limits their applications in resource-constrained platforms (e.g. mobile devices and robots). The research team notices that there is a strong mathematical connection between certain types of neural networks and analog integrated circuits. It is also known that the EDA (electronic design automation) field has 50 years of successful history of modeling, simulating, verifying and optimizing analog integrated circuits. Therefore, this project aims to substantially enrich the algorithms and theoretical understanding of neural networks by leveraging the principled approaches in the EDA community. This research will support the cross-disciplinary development of a diverse cohort of graduate and undergraduate students at the University of California at Santa Barbara, the University of California at San Diego, and the Massachusetts Institute of Technology. Several graduate-level courses on computational methods, data science and artificial intelligence are being created or enriched. The research team willis also collaborating with industry to ensure effective technology transfers.This project focuses on certain types of deep neural networks (e.g., residual neural networks, recurrent neural networks and normalizing flows) that can be described as ordinary differential equations. The technical aims of the project are divided into three thrusts. The first thrust investigates the training and compression algorithms of deep neural networks from circuit simulation and modeling perspectives. Specifically, parallel training algorithms are being developed for neural networks by borrowing the idea from parallel circuit simulation. Hardware-friendly neural-network compression algorithms are being developed from the perspective of circuit model order reduction, thereby enabling energy-efficient and real-time inference of deep neural networks. The second thrust investigates probabilistic and accurate verification techniques for the robustness of deep neural networks from circuit uncertainty quantification perspectives. Specifically, high-confidence and tighter verification bounds are being developed to describe the reachable set of a deep neural network by leveraging the hierarchical and non-Monte-Carlo techniques in analog circuit uncertainty quantification. The third thrust aims to improve the robustness of a deep neural network from the perspective of analog circuit yield optimization. In this final thrust, two ideas are being explored: (1) pre-silicon yield optimization techniques for robust neural network training, and (2) post-silicon self-healing techniques for robustness improvement of a trained neural network.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.

深度神经网络在许多工程领域取得了巨大成功，包括但不限于图像分类、语音识别、推荐系统和自动驾驶。然而，他们面临两大挑战。首先，许多神经网络模型并不稳健，即当输入数据经历非常少量的扰动时，神经网络可能会产生不准确的结果。其次，生成和部署大型神经网络的巨大成本限制了它们在资源受限平台（例如移动设备和机器人）中的应用。研究小组注意到，某些类型的神经网络和模拟集成电路之间存在很强的数学联系。据了解，EDA（电子设计自动化）领域拥有50年的模拟集成电路建模、仿真、验证和优化的成功历史。因此，该项目旨在通过利用 EDA 社区的原则方法来大幅丰富神经网络的算法和理论理解。这项研究将支持加州大学圣巴巴拉分校、加州大学圣地亚哥分校和麻省理工学院的不同研究生和本科生的跨学科发展。一些关于计算方法、数据科学和人工智能的研究生课程正在创建或丰富。研究团队还将与业界合作，以确保有效的技术转让。该项目重点研究某些类型的深度神经网络（例如残差神经网络、循环神经网络和归一化流），这些网络可以被描述为常微分方程。该项目的技术目标分为三个主旨。第一个主旨从电路仿真和建模的角度研究深度神经网络的训练和压缩算法。具体来说，通过借鉴并行电路模拟的思想，正在为神经网络开发并行训练算法。硬件友好的神经网络压缩算法正在从电路模型降阶的角度进行开发，从而实现深度神经网络的节能和实时推理。第二个主旨从电路不确定性量化的角度研究深度神经网络鲁棒性的概率和精确验证技术。具体来说，通过利用模拟电路不确定性量化中的分层和非蒙特卡罗技术，正在开发高置信度和更严格的验证界限来描述深度神经网络的可达集。第三个推力旨在从模拟电路良率优化的角度提高深度神经网络的鲁棒性。在这最后的推动力中，正在探索两个想法：（1）用于鲁棒神经网络训练的投片前良率优化技术，以及（2）用于提高经过训练的神经网络的鲁棒性的投片后自我修复技术。该奖项反映了 NSF 的法定使命，并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，被认为值得支持。