BOLT-PM: An Adaptive Bayesian Optimization Framework for Latency-Sensitive Task and Power Management in AIoT Devices

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Abstract
The integration of AI tasks into Artificial Intelligence of Things (AIoT) devices has gained significant attention for its ability to reduce transmission latency and enhance data privacy compared to traditional cloud-based solutions. However, heterogeneous AIoT platforms face the challenge of balancing stringent latency constraints with energy efficiency under dynamic AI workloads. To address this issue, we propose BOLT-PM, a Bayesian Optimization framework for Latency-sensitive Task and Power Management, which dynamically allocates computing resources and optimizes power consumption in AIoT devices. We design a multi-branch neural network to accurately predict task execution times under varying resource constraints, enabling precise and adaptive performance estimation. We further enhance the Bayesian optimization process by embedding a variational autoencoder (VAE) to construct a smooth latent representation of the search space, thereby accelerating convergence and improving optimization stability. We implement BOLT-PM as a lightweight, platform-agnostic runtime framework that continuously adapts to workload fluctuations, maintaining latency guarantees while minimizing energy consumption. We evaluate BOLT-PM on several commercial AIoT boards, including RK3588, Jetson TX2, and Raspberry Pi, and compare it against classical heuristic methods and state-of-the-art approaches. Experimental results show that BOLT-PM achieves substantial energy savings while ensuring low-latency AI task execution, demonstrating its effectiveness as a robust and energy-efficient solution for power-aware AIoT applications.
Funding Information
  • the (STI2030)

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