Software-Hardware Codesign for Machine Learning Workloads, a Workshop at MLSyS 2020
Workshop SEI SpeakingMar 4, 2020 · Austin, TX
Summary
Bridging the Gap Between Software and Hardware
More Information
https://resources.sei.cmu.edu/news-events/events/MLSyS-2020-workshop/index.cfmAgenda
Machine learning development workflows today involve the siloed design and optimization of task-specific software for a limited number of fixed hardware options. As a result, hardware and software are seen as individual components where the impact of either SW or HW on each other cannot be optimized or assessed jointly. This abstraction leads to computationally inefficient machine learning workloads.
Presenters
Dr. Christopher Aberger - Director, Software Engineering - SambaNova Systems
Dr. Dennis Abts - Chief Architect - Groq
Professor Luca Carloni - Columbia University
Mayank Daga - Director, Deep Learning Software - AMD
Matt Fyles - VP Software - Graphcore
Professor Tze Meng Low - Carnegie Mellon University
James Moawad - Technical Solution Specialist - Intel
Nick Ni - Director of Product Marketing, AI and Software - Xilinx
Dr. Thomas Rondeau - Program Manager - DARPA
Dr. Kshitij Sudan - Principle Solutions Architect - Arm
Professor Michael Taylor - University of Washington
Dr. Natalia Vassilieva - Technical Product Manager - Cerebras Systems
Dr. Jeffrey Vetter - Future Technologies Group Leader - Oak Ridge National Laboratory