PMID- 35853057
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20240217
IS  - 2162-2388 (Electronic)
IS  - 2162-237X (Linking)
VI  - 35
IP  - 2
DP  - 2024 Feb
TI  - Multiarmed Bandit Algorithms on Zynq System-on-Chip: Go Frequentist or Bayesian?
PG  - 2602-2615
LID - 10.1109/TNNLS.2022.3190509 [doi]
AB  - Multiarmed 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, Internet of Things (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 pseudorandom number 
      generator (PRNG)-based architecture and efficiently realize the TS algorithm on 
      Zynq SoC. In practice, the type of arms distribution (e.g., Bernoulli, Gaussian) 
      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 in an unknown 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 
      codesign approaches. We demonstrate the superiority of the RI-MAB algorithm and 
      its architecture over the TS and UCB algorithms.
FAU - Santosh, S V Sai
AU  - Santosh SVS
FAU - Darak, Sumit J
AU  - Darak SJ
LA  - eng
PT  - Journal Article
DEP - 20240205
PL  - United States
TA  - IEEE Trans Neural Netw Learn Syst
JT  - IEEE transactions on neural networks and learning systems
JID - 101616214
SB  - IM
EDAT- 2022/07/20 06:00
MHDA- 2022/07/20 06:01
CRDT- 2022/07/19 13:43
PHST- 2022/07/20 06:01 [medline]
PHST- 2022/07/20 06:00 [pubmed]
PHST- 2022/07/19 13:43 [entrez]
AID - 10.1109/TNNLS.2022.3190509 [doi]
PST - ppublish
SO  - IEEE Trans Neural Netw Learn Syst. 2024 Feb;35(2):2602-2615. doi: 
      10.1109/TNNLS.2022.3190509. Epub 2024 Feb 5.