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Session

Research Manuscript: A Melange of Machine Learning Frameworks for Optimization
Event TypeResearch Manuscript
Virtual Programs
Presented In-Person
Keywords
AI/ML System Design
Topics
Design
TimeThursday, December 9th11:00am - 12:00pm PST
Location3014
DescriptionThe landscape of machine learning systems is evolving very fast, and many optimizations are needed across algorithm, compiler, and hardware accelerator designs. In this session, we highlight frameworks for machine learning system optimization covering four topics: memory-efficient graph neural network design, neural hardware-compiler co-design, energy-efficient unsupervised spiking neural network framework, and Bayesian neural network hardware accelerator.