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 (DNN) 大致模仿人脑，在准确解释感官数据和识别模式方面取得了巨大成功。然而，它们尚未针对当前和未来的低功耗多传感器应用进行探索，例如物联网 (IoT)、可穿戴健康和移动智能设备。嵌入式探索的根本问题是当前的 DNN 模型非常复杂，这使得在硬件资源和功耗预算有限的嵌入式系统中部署它们具有挑战性。 该项目研究了 DNN 网络建模、稀疏化和软件逼近技术的新颖且变革性的方法，称为 DeepMatter。该研究开发了新的架构来设计可编程的特定领域多核平台，该平台可实现优化的网络，并提供嵌入式 DNN 实现所需的性能、可扩展性、可编程性和功效要求。应用程序接口（API）的设计将允许设计人员快速原型设计并部署下一代复杂的智能应用程序。为了进行演示，将在 DeepMatter 上评估五种应用，包括用于癫痫和痛苦检测的多生理处理、多模式辅助设备、空气质量监测和基于视觉的态势感知。该研究项目的成功将产生小型且节能的可穿戴/移动计算设备，这些设备可以在传感器上对原始数据进行知识提取和分类，而无需将大量原始数据发送到云端进行处理。这可以彻底改变医疗保健、交通、生态、监控、公共事业等多个领域。软件模型、硬件和工具将可供研究界制作原型并评估不同的应用程序。该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，涉及初高中学生和教师以及本科生和研究生。