Intelligent and Reconfigurable Architecture for KL Divergence Based Online Machine Learning Algorithm
This enables faster and more adaptable deployment in robotics and wireless applications, though it is incremental as it optimizes an existing algorithm for hardware integration.
The paper tackles the complexity of the KLUCB algorithm for online machine learning by mapping it efficiently onto a system-on-chip with a reconfigurable architecture, achieving a 10-factor improvement in latency and throughput after switching to a lightweight UCB method.
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.