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Upcoming Seminars

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Vector Symbolic Architectures for Autonomous Science

Date: October 8, 2021 1:00 pm Pacific
Speaker: Michael Furlong (University of Waterloo)

Automating exploration often involves information theoretic cost functions which can be expensive to compute. Planetary missions are constrained by size, weight, and power concerns, as well as environmental conditions, that limit the type and amount of computing that can be deployed on these missions.

Neuromorphic computing promises to reduce power requirements needed for deploying high-performance computing, enabling constrained systems to be more capable, but they can be challenging to program. Vector Symbolic Architectures, originally developed in the context of cognitive modelling, have proven useful as a paradigm for programming these computers.

In this talk we will be discussing how a particular Vector Symbolic Architecture can be used to efficiently execute two tasks commonly found in autonomous science applications: anomaly detection and Bayesian optimization. We will show how these algorithms can be computed with time and memory complexity that is constant in the number of observations collected, making them favourable algorithms for long-term operations in resource constrained computing environments.

Bayesian Techniques for Accelerator Characterization and Control

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