# Efficient Implementation of LinearUCB through Algorithmic Improvements and Vector Computing Acceleration for Embedded Learning Systems

arXiv: https://arxiv.org/abs/2501.13139 · HTML: https://mlsanity.com/paper/2501.13139/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 39 references that are in the map.
- First posted on arXiv 22 Jan 2025.

## Builds on (most cited first, 7 of 7)

- [Machine Learning at the Network Edge: A Survey](https://mlsanity.com/paper/1908.00080/) (2019)
- [Contextual User Browsing Bandits for Large-Scale Online Mobile Recommendation](https://mlsanity.com/paper/2008.09368/) (2020)
- [Adaptive Lambda Least-Squares Temporal Difference Learning](https://mlsanity.com/paper/1612.09465/) (2016)
- [Ensemble Active Learning by Contextual Bandits for AI Incubation in Manufacturing](https://mlsanity.com/paper/2310.06306/) (2023)
- [Spatio-temporal Edge Service Placement: A Bandit Learning Approach](https://mlsanity.com/paper/1810.03069/) (2018)
- [Edge Computing in the Dark: Leveraging Contextual-Combinatorial Bandit and Coded Computing](https://mlsanity.com/paper/1912.09512/) (2019)
- [Klessydra-T: Designing Vector Coprocessors for Multi-Threaded Edge-Computing Cores](https://mlsanity.com/paper/2007.09109/) (2020)

## Built on by (most cited first, 4 of 4)

- [HD-CB: The First Exploration of Hyperdimensional Computing for Contextual Bandits Problems](https://mlsanity.com/paper/2501.16863/) (2025)
- [Scalable LinUCB: Low-Rank Design Matrix Updates for Recommenders with Large Action Spaces](https://mlsanity.com/paper/2510.19349/) (2025)
- [Learning Over Dirty Data with Minimal Repairs](https://mlsanity.com/paper/2503.13921/) (2025)
- [Contextual Bandits for Resource-Constrained Devices using Probabilistic Learning](https://mlsanity.com/paper/2605.13346/) (2026)

## Abstract (arXiv)

As the Internet of Things expands, embedding Artificial Intelligence algorithms in resource-constrained devices has become increasingly important to enable real-time, autonomous decision-making without relying on centralized cloud servers. However, implementing and executing complex algorithms in embedded devices poses significant challenges due to limited computational power, memory, and energy resources. This paper presents algorithmic and hardware techniques to efficiently implement two LinearUCB Contextual Bandits algorithms on resource-constrained embedded devices. Algorithmic modifications based on the Sherman-Morrison-Woodbury formula streamline model complexity, while vector acceleration is harnessed to speed up matrix operations. We analyze the impact of each optimization individually and then combine them in a two-pronged strategy. The results show notable improvements in execution time and energy consumption, demonstrating the effectiveness of combining algorithmic and hardware optimizations to enhance learning models for edge computing environments with low-power and real-time requirements.