Energy efficient reconfigurable accelerator for monocular underwater image enhancement
Date:
Image enhancement is crucial for precise underwater visual perception, while deep learning (DL) techniques outperform conventional approaches in diverse environments. However, the deployment of energy-efficient DL inference on autonomous underwater vehicles (AUVs) remains challenging due to strict space and power constraints. This paper presents a hardware-software codesign for real-time underwater image enhancement on an FPGA. A low-power image enhancement accelerator is implemented using a hardware-friendly activation function and employing a lightweight neural network through an automated High-Level Synthesis (HLS) toolchain with extended functionalities. Experimental results demonstrate that the implemented FPGA accelerator delivers exceptional real-time performance and energy efficiency, achieving a ∼ 30× speedup and ∼ 60% less power consumption compared to a high-performance edge computing platform, and complete co-design framework of deep learningbased underwater image enhancement targeting automated FPGA accelerator generation for edge deployment.
