CAREER: DeepMatter: A Scalable and Programmable Embedded Deep Neural Network

职业：DeepMatter：可扩展且可编程的嵌入式深度神经网络

• 批准号: 2348983
• 负责人: Tinoosh Mohsenin
• 金额: $ 47.51万
• 依托单位: Johns Hopkins University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2348983&HistoricalAwards=false
• 关键词: CAREER; DeepMatter; Scalable; Programmable; Embedded

Deep neural networks (DNNs), modeled loosely after the human brain, have shown tremendous success to accurately interpret sensory data and recognize patterns. However, they have not been explored for current and future low power multi-sensor applications, such as Internet of Things (IoT), wearable health and mobile smart devices. The fundamental problem with embedded exploration is that current DNN models are very complex, making them challenging to deploy in embedded systems with limited hardware resources and power budgets. The project investigates novel and transformative methodologies for DNN network modeling, sparsification, and approximation techniques in software, termed DeepMatter. The research develops new architectures to design a programmable domain-specific many-core platform that implements the optimized network and provides performance, scalability, programmability, and power efficiency requirements necessary for embedded DNN implementations. An application program interface (API) will be designed to allow designers to rapidly prototype and deploy the next generation of sophisticated and intelligent applications. For demonstration, five applications including multi-physiological processing for seizure and distress detection, multi-modal assistive device, air quality monitoring and vision-based situational awareness will be evaluated on DeepMatter. The success of this research project will result in small and energy efficient wearable/mobile computing devices which can perform knowledge extraction and classification on raw data at the sensor without sending massive raw data to the cloud for processing. This can revolutionize several fields including healthcare, transportation, ecology, surveillance, public utilities. Software models, hardware and tools will be available for the research community to prototype and evaluate different applications. This research provides a multidisciplinary platform for educational objectives of developing embedded smart processors and involves middle and high school students and teachers as well as undergraduate and graduate students.

深度神经网络（DNNS）以人脑的宽松建模，已显示出巨大的成功，可以准确解释感觉数据并识别模式。但是，尚未针对当前和未来的低功率多传感器应用程序进行探索，例如物联网（IoT），可穿戴健康和移动智能设备。嵌入式探索的基本问题是，当前的DNN模型非常复杂，这使得它们在具有有限的硬件资源和功率预算有限的嵌入式系统中挑战。 该项目调查了用于DeepMatter的软件中DNN网络建模，稀疏和近似技术的新颖和变革性方法。该研究开发了新的体系结构来设计一个可编程域特异性多核平台，该平台实现了优化的网络并提供嵌入式DNN实现所需的性能，可伸缩性，可编程性和功率效率要求。应用程序界面（API）将旨在允许设计人员快速原型和部署下一代复杂和智能应用程序。为了进行演示，将在DeepMatter上评估五个应用，包括用于癫痫发作和遇险检测的多物理学处理，多模式辅助设备，空气质量监测和基于视觉的情境意识。该研究项目的成功将导致小型和节能的可穿戴/移动计算设备，这些设备可以在传感器的原始数据上进行知识提取和分类，而无需将大量的原始数据发送到云中进行处理。这可以彻底改变许多领域，包括医疗保健，运输，生态，监视，公用事业。软件模型，硬件和工具将用于研究社区，以原型和评估不同的应用程序。这项研究为开发嵌入式智能处理器的教育目标提供了一个多学科平台，并涉及中学生，老师以及本科生和研究生。