  Multi-armed Bandit (MAB) algorithms identify the best arm among multiple arms
via exploration-exploitation trade-off without prior knowledge of arm
statistics. Their usefulness in wireless radio, IoT, and robotics demand
deployment on edge devices, and hence, a mapping on system-on-chip (SoC) is
desired. Theoretically, the Bayesian approach-based Thompson Sampling (TS)
algorithm offers better performance than the frequentist approach-based Upper
Confidence Bound (UCB) algorithm. However, TS is not synthesizable due to Beta
function. We address this problem by approximating it via a pseudo-random
number generator-based approach and efficiently realize the TS algorithm on
Zynq SoC. In practice, the type of arms distribution (e.g., Bernoulli,
Gaussian, etc.) is unknown and hence, a single algorithm may not be optimal. We
propose a reconfigurable and intelligent MAB (RI-MAB) framework. Here,
intelligence enables the identification of appropriate MAB algorithms for a
given environment, and reconfigurability allows on-the-fly switching between
algorithms on the SoC. This eliminates the need for parallel implementation of
algorithms resulting in huge savings in resources and power consumption. We
analyze the functional correctness, area, power, and execution time of the
proposed and existing architectures for various arm distributions, word-length,
and hardware-software co-design approaches. We demonstrate the superiority of
the RI-MAB over TS and UCB only architectures.

Systems and Control;Hardware Architecture;Machine Learning;Systems and Control