SBIR Phase I: Enabling Real-Time AI on End Devices through Compression-Compilation Co-Design

SBIR 第一阶段：通过压缩编译协同设计在终端设备上启用实时人工智能

• 批准号: 2104298
• 负责人: Xipeng Shen
• 金额: $ 25.47万
• 依托单位: COCOPIE LLC
• 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: 2021-07-01 至 2022-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2104298&HistoricalAwards=false
• 关键词: SBIR; Phase; Enabling; Real; Time

The broader impact of this Small Business Innovation Research (SBIR) Phase I project is in the array of new opportunities it creates for expanding uses of machine intelligence. By providing an efficient way to transform deep learning models to best fit the constraints on end devices and real-time applications, this project will shorten the time to market for artificial intelligence applications by orders of magnitude, and hence significantly accelerate the development and deployment of intelligent software in health, commerce, financial, defense, social networks, and many other areas.This Small Business Innovation Research (SBIR) Phase I project aims to address the important barriers for efficient development of real-time Artificial Intelligence applications on end devices (smartphones, drones, etc.). It does this through a breakthrough technology, compression-compilation co-design. Compression and compilation are the two key steps in fitting a deep learning model on a hardware for efficient execution. Model compression reduces the size of deep learning models; compilation generates executable codes from a given deep learning model. The principle of compression-compilation co-design is to design the two components for AI in a hand-in-hand manner. The technology uses a novel approach to synergize a set of novel model compression methods with compression-aware code compilation techniques. The result is a technology that achieves several-fold higher artificial intelligence model compression rates over the state of the art, several times faster speed, better energy efficiency, and satisfying accuracy.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.

这个小型企业创新研究（SBIR）第一阶段项目的更广泛影响在于它为扩大机器智能的使用创造了一系列新的机会。通过提供一种有效的方法来转换深度学习模型，以最好地适应终端设备和实时应用的限制，该项目将缩短人工智能应用的上市时间，从而大大加快健康，商业，金融，国防，社交网络，小企业创新研究（SBIR）第一阶段项目旨在解决终端设备（智能手机、无人机等）上实时人工智能应用高效开发的重要障碍。它通过一项突破性的技术--压缩-编译协同设计来实现这一点。压缩和编译是在硬件上安装深度学习模型以实现高效执行的两个关键步骤。模型压缩可以减小深度学习模型的大小;编译可以从给定的深度学习模型生成可执行代码。压缩-编译协同设计的原则是以手拉手的方式为AI设计这两个组件。该技术使用一种新的方法来协同一组新的模型压缩方法与压缩感知代码编译技术。其结果是，该技术实现了比现有技术高出数倍的人工智能模型压缩率、数倍的速度、更高的能源效率和令人满意的准确性。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。