CISE-ANR: SHF: Small: CHAMELEON: CompreHending And Mitigating Error in AnaLog ImplEmentations of On-Die Neural Networks

CISE-ANR：SHF：小：CHAMELEON：理解并减轻片上神经网络模拟实现中的错误

• 批准号: 2214934
• 负责人: Yiorgos Makris
• 金额: $ 62.38万
• 依托单位: University of Texas at Dallas
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2214934&HistoricalAwards=false
• 关键词: CISE; ANR; SHF; Small; CHAMELEON

Considering the widespread deployment of machine-learning hardware in myriads of modern-life applications, ensuring its reliable and safe operation is crucial to advance the national prosperity and welfare and to secure the national defense. While software and/or digital hardware implementations of neural networks currently enjoy the lion’s share of the market, a number of emerging realities are necessitating the development and deployment of analog neural networks. Specifically, the exponential growth of sensory data from world-machine interfaces, known as the analog data deluge, along with the area, power consumption and response-time constraints of distributed edge-computing systems, necessitate autonomous sensing, perception, reasoning and rapid action. While analog neural-network implementations promise to deliver this ability, their robustness and reliability are susceptible to parametric differences introduced by manufacturing process variation, operational conditions variation, as well as silicon aging. Accordingly, this project seeks to enable robust and resilient operation of analog neural networks and the applications wherein they are deployed, as well as to educate the next generation of engineers on the risks and remedies of using analog machine-learning hardware.At a technical level, this project combines state-of-the-art methods for designing, testing, and calibrating analog integrating circuits, with advanced concepts from training machine-learning models, in an effort to comprehend the vulnerability of analog neural networks, develop error-mitigation solutions, and assess their effectiveness. To this end, the research activities undertaken by this project include (i) investigation and mitigation of the impact of parametric and operational differences on machine-learning models implemented as analog neural networks, (ii) development of methods for specifying and evaluating the learning capacity of such designs, and (iii) demonstration of the efficiency of the proposed methods through custom analog neural-network experimentation platforms.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.

考虑到无数现代应用程序中机器学习硬件的宽度部署，确保其可靠和安全的运营对于促进国家繁荣和福利和确保国防而至关重要。尽管神经网络的软件和/或数字硬件实现当前享有市场份额，但需要许多新兴现实才能开发和部署模拟神经元网络的开发和部署。具体而言，来自世界机界面的感觉数据的指数增长，被称为模拟数据洪水，以及该区域，功耗和响应时间约束，对分布式边缘计算系统的响应时间约束，必要的自主灵敏度，感知，推理和快速行动。虽然模拟神经网络实现有望提供这种能力，但它们的鲁棒性和可靠性容易受到制造过程变化，操作条件变化以及硅老化引入的参数差异。据此，该项目旨在实现模拟神经网络以及部署的应用程序的稳健和抵抗操作理解模拟神经元网络，开发错误解决方案的脆弱性以及评估其有效性。为此，该项目开展的研究活动包括（i）调查和缓解参数和操作差异对作为模拟神经元网络实施的机器学习模型的影响，（ii）开发通过裁定此类设计的学习能力的方法，以及（iii）授权的方法，以及（iii）授予定制的Analog Neuro-n-Net效率。法定使命，并使用基金会的知识分子优点和更广泛的影响标准通过评估被认为是宝贵的支持。