This paper proposes PixelPrune, an approach to address two primary challenges in artificial intelligence of things (AIoT) vision systems: (1) the energy-intensive analog-to-digital converters (ADCs) required in the sensing unit for converting analog pixel arrays to digital tensors, and (2) the high data transfers between the sensing unit and computing unit. Our proposed solution involves the implementation of an in-sensor binary segmentation model on analog memristive crossbars to identify the important pixels and prune out the background information. Additionally, we propose a data transfer scheme that adaptively selects between dense and sparse data transfer formats based on the sparsity ratio measured from the segmentation mask obtained by the segmentation model. Our results demonstrate that the proposed object detection system achieves significant energy savings along with a considerable up to 95% reduction in data transfer, all while maintaining high accuracy.

This paper addresses the challenge of deploying machine learning (ML)-based segmentation models on edge platforms to facilitate real-time scene segmentation for Autonomous Underwater Vehicles (AUVs) in underwater cave exploration and mapping scenarios. We focus on three ML models-U-Net, CaveSeg, and YOLOv8n-deployed on four edge platforms: Raspberry Pi-4, Intel Neural Compute Stick 2 (NCS2), Google Edge TPU, and NVIDIA Jetson Nano. Experimental results reveal that mobile models with modern architectures, such as YOLOv8n, and specialized models for semantic segmentation, like U-Net, offer higher accuracy with lower latency. YOLOv8n emerged as the most accurate model, achieving a 72.5 Intersection Over Union (IoU) score. Meanwhile, the U-Net model deployed on the Coral Dev board delivered the highest speed at 79.24 FPS and the lowest energy consumption at 6.23 mJ. The detailed quantitative analyses and comparative results presented in this paper offer critical insights for deploying cave segmentation systems on underwater robots, ensuring safe and reliable AUV navigation during cave exploration and mapping missions.