Multi-armed Bandit on System-on-Chip - Go Frequentist or Bayesian?

Learn about multi-armed bandit-based online learning algorithms and their practical implementations in a technical talk delivered by Associate Professor Sumit J Darak from IIIT-Delhi. Explore the implementation challenges and solutions for deploying these algorithms on System-on-Chip (SoC) platforms for wireless radio, IoT, and robotics applications. Dive into detailed architectural designs for key algorithms including Upper Confidence Bound (UCB), Kullback Leibler UCB (KLUCB), and Thompson Sampling. Examine hardware-software co-design approaches and fixed-point analysis techniques for efficient SoC implementation. Discover intelligent reconfigurable architecture strategies for handling dynamic environments and maintaining system flexibility. The presentation provides valuable insights into both frequentist and Bayesian approaches to multi-armed bandit problems in edge computing contexts.