RTML: Large: Continuous Adaptation for Decision Streams

Stanford University, Stanford CA

Investigators

Abstract

Systems that can efficiently make real-time decisions based on large-scale data streams will impact broad areas of daily life, including autonomous vehicles, personal assistants, medicine, and fraud detection tools. Such systems have become critical as machine learning is increasingly tasked with making richer decisions over constantly-changing large data streams for both consumer and industry applications. This project seeks to develop hardware-software systems capable of making such real-time decisions over large data streams while flexibly and continuously adapting to changes in their environment. This project will also support redesign of courses on hardware accelerators and parallel computing at Stanford University, with large-scale data streaming systems as a central driver. These courses are designed to provide students with a sufficiently strong background to engage in systems and machine learning research, thus enabling a diverse and much desired US workforce in an area of technology of current importance. This project will create tools and techniques for large-scale data streaming systems by producing innovations spanning software, hardware, and machine learning. Modern machine learning requires a vast amount of labeled data, and streaming scenarios only increase this requirement. To handle the need for more data, techniques for automatically labeling data (and in particular, temporal data) under real-time constraints will be developed. Because the environment for data streaming systems is constantly changing, the proposed project seeks to continuously adapt and specialize models to the current environment, leading to vastly improved efficiency. Real-time data streaming systems will require hardware that achieves both exceptional efficiency, as well as provides sufficient flexibility to support both real-time model training and inference; this project will develop such hardware. These innovations will be demonstrated and evaluated on two applications: next-generation video stream processing in autonomous vehicle, medical and industrial domains and smart routers for networking systems. The project will also collaborate with a synergistic DARPA program for related hardware development. 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.