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.

这项小型企业创新研究（SBIR）I阶段项目的更广泛影响在于它为扩大机器智能使用而创造的新机会。通过提供一种有效的方法来改变深度学习模型以最佳地适合最终设备和实时应用的约束，该项目将缩短按数量级的人工智能应用上市的时间，因此可以显着加速智能软件在健康，商业，商业，财务，国防，社交网络以及许多小型商业创业方面的开发中的智能软件的开发和部署。最终设备（智能手机，无人机等）上的实时人工智能应用程序。它通过突破性技术，压缩兼容共同设计来做到这一点。压缩和汇编是将深度学习模型拟合在硬件上以进行有效执行的两个关键步骤。模型压缩减少了深度学习模型的大小；编译从给定的深度学习模型生成可执行的代码。压缩兼容共同设计的原理是以手持方式为AI设计两个组件。该技术使用一种新颖的方法来协同一组新型的模型压缩方法，采用压缩感知的代码编译技术。结果是一项技术，该技术可实现与最新情况相比，速度更快的速度，更好的能源效率和令人满意的准确性，该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的审查标准来评估，这是值得通过评估来支持的。