  Online machine learning (OML) algorithms do not need any training phase and
can be deployed directly in an unknown environment. OML includes multi-armed
bandit (MAB) algorithms that can identify the best arm among several arms by
achieving a balance between exploration of all arms and exploitation of optimal
arm. The Kullback-Leibler divergence based upper confidence bound (KLUCB) is
the state-of-the-art MAB algorithm that optimizes exploration-exploitation
trade-off but it is complex due to underlining optimization routine. This
limits its usefulness for robotics and radio applications which demand
integration of KLUCB with the PHY on the system on chip (SoC). In this paper,
we efficiently map the KLUCB algorithm on SoC by realizing optimization routine
via alternative synthesizable computation without compromising on the
performance. The proposed architecture is dynamically reconfigurable such that
the number of arms, as well as type of algorithm, can be changed on-the-fly.
Specifically, after initial learning, on-the-fly switch to light-weight UCB
offers around 10-factor improvement in latency and throughput. Since learning
duration depends on the unknown arm statistics, we offer intelligence embedded
in architecture to decide the switching instant. We validate the functional
correctness and usefulness of the proposed architecture via a realistic
wireless application and detailed complexity analysis demonstrates its
feasibility in realizing intelligent radios.
