Fast AI: Data Center & Edge Computing

The AI revolution has been enabled by the availability of vast amounts of labeled data, novel algorithms, and computer performance. But long computer-in-the-loop development cycles inhibit humans from inventing and deploying creative AI solutions. Moreover, the end of Moore’s has curtailed the historical ability of semiconductor technology to deliver performance. AI performance increasingly relies on hardware architecture, software, and algorithms. The Fast AI project focuses on developing a foundation for quickly building AI solutions, enabling performance and portability on both modern and legacy hardware platforms. We innovate in the areas of programming languages, compiler technologies, comprehensive instrumentation, analytical productivity tools, and parallel algorithms.

Team Leiserson
– Charles E. Leiserson (MIT PI)
– Tao B. Schardl (MIT)
– Neil Thompson (MIT)
– Vijay Gadepally (MIT Lincoln Laboratory Lead)
– Matthew Schuyler (DAF Liaison)

ML and Generative AI Enhanced Data Systems, Discovery, and Applications

A core requirement for AI techniques to be successful is high quality data. Preparing systems to be “AI ready” involves collecting and parsing raw data for subsequent ingest, scan, query and analysis. This project will develop ML-enhanced database technologies to reduce storing and processing costs while enabling data sharing amongst various database silos. Additionally, we will develop an outlier detection engine to identify temporal anomalies amongst complex event streams from multiple sources.

Team Kraska
– Tim Kraska (MIT PI)
– Vijay Gadepally (MIT Lincoln Laboratory Lead)
– Matthew Schuyler (DAF Liaison)
 

Are you up for a Challenge?

Maneuver ID Challenge: Through the AI Accelerator, the U.S. Air Force released a dataset from Pilot Training Next (PTN) of Air Force pilots and trainees flying in virtual reality simulators. To enable AI coaching and automatic maneuver grading in pilot training, the Air Force seeks to automatically identify and label each maneuver flown in this dataset from a catalog of around 30 maneuvers. Challenge solution help advance the state-of-the-art in flying training. Learn more about the challenge here.  

Datacenter Challenge: Using a massive dataset containing Slurm Workload Manager scheduler data and job-specific time series data, the AI Accelerator is looking for innovative AI/ML techniques that will facilitate the development of tools to detect, explain, recover, and potentially predict outliers and ultimately mitigate system failure in complex multi-sensor systems. This challenge will gain a better understanding for optimizing job performance and datacenter management speed and efficiency, leading to reduced energy costs and carbon emissions and making AI more sustainable. Learn more about the challenge here. 

To learn more about Fast AI: Data Center & Edge Computing research and other AI Accelerator projects, view our published research here.