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Machine Learning Systems

Designing systems that are largely defined by the execution of machine learning workloads present new open problems that straddle the domains of systems, hardware and artificial intelligence. Cyber-physical systems are a prime example of this emerging category. This activity considers the needs of the next generation of machine-learning-centric systems in terms of: design, interfaces and abstractions; parallel, distributed and scalable learning/inference algorithms; hardware co-design for efficiency and high-utilization; and finally, interpretability, security and testing.

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