Abstract: Modern computing systems must meet multiple---often conflicting---goals; e.g., high-performance and low energy consumption. The current state-of-practice involves ad hoc, heuristic solutions to such system management problems that offer no formally verifiable behavior and must be rewritten or redesigned wholesale as new computing platforms and constraints evolve. In this talk, I will discuss my research on building self-aware computing systems that combine machine learning and control theory to handle system goals and constraints in a fundamental way, starting with rigorous mathematical models and ending with real software and hardware implementations that have formally analyzable behavior and can be re-purposed to address new problems as they emerge. I will first cover methodology for building these systems and then show how they can be applied to control accuracy/resource tradeoffs for AI-based computations in both scientific computing and low-power sensing.
Bio: Henry Hoffmann is somehow the Liew Family Chair of the Department of Computer Science at the University of Chicago. He received the Presidential Early Career Award for Scientists and Engineers (PECASE) in 2019. He was granted early tenure in 2018. He is a member of the Samsung Security Hall of Fame and of the (unofficial) ASPLOS Hall of Fame. He has a Test of Time Honorable Mention from FSE 2021 for his work on Loop Perforation and approximate computing. He received the DOE Early Career Award in 2015. At Chicago he leads the Self-aware computing group (or SEEC project) and conducts research on adaptive techniques for power, energy, accuracy, security, and performance management in computing systems.
He completed a PhD in Electrical Engineering and Computer Science at MIT in 2013 where his research on self-aware computing was named one of ten "World Changing Ideas" by Scientific American in December 2011. He received his SM degree in Electrical Engineering and Computer Science from MIT in 2003. As a Masters student he worked on MIT's Raw processor, one of the first multicores.
Along with other members of the Raw team, he spent several years at Tilera Corporation, a startup which commercialized the Raw architecture and created one of the first manycores (Tilera was sold for $130M in 2014). His implementation of the BDTI Communications Benchmark (OFDM) on Tilera's 64-core TILE64 processor still has the highest certified performance of any programmable processor.
In 1999, he received his BS in Mathematical Sciences with highest honors and highest distinction from UNC Chapel Hill.
