Dr. Christopher Aberger - Director, Software Engineering - SambaNova Systems

Title: Abstract: In many applications traditional software development is being replaced by machine learning generated models resulting in accuracy improvements and deployment advantages. This fundamental shift in how we develop software is known as Software 2.0. The continued success of Software 2.0 will require efficient and flexible computer hardware optimized for the dataflow computational graphs at the core of machine learning. In this talk, we will discuss the design of high-performance dataflow computer architectures for machine learning. Our vertically integrated approach to machine learning performance combines new machine learning algorithms, new domain-specific languages, advanced compilation technology and software-defined hardware.

Keywords: dataflow computational graph, domain specific languages, compiler technology, software defined hardware Bio: Dr. Christopher Aberger is a director of software engineering at SambaNova Systems where he leads the machine learning team. Christopher works on efficient training algorithms for new and emerging hardware architectures. He received his Ph.D. degree in Computer Science from Stanford University where he studied the intersection of graph, database, and machine learning systems; this work received a Best Of award at VLDB in 2016 and an invited TODS article in 2017.