Hardware Utilization and Inference Performance of Edge Object Detection Under Fault Injection

2026-03-19

AI Summary

This work addresses the lack of systematic investigation into hardware reliability under input degradation when deploying deep learning models on resource-constrained edge devices. The authors propose a decoupled fault injection framework that, for the first time, integrates large language models (LLMs) with latent diffusion models (LDMs) to generate realistic degraded inputs. Using this framework, they conduct multidimensional hardware monitoring—covering CPU/GPU utilization, memory usage, power consumption, throughput, and temperature—on a Jetson Nano platform running TensorRT-optimized YOLOv10s, YOLOv11s, and YOLO2026n models. Experimental results demonstrate that even under severely degraded inputs, TensorRT inference maintains stable GPU occupancy, controlled temperature rise, and safe power consumption, while memory usage exhibits predictable release patterns after warm-up, providing empirical evidence to guide robust edge AI system design.

Abstract

As deep learning models are deployed on resource constrained edge platforms in autonomous driving systems, reli able knowledge of hardware behavior under resource degradation becomes an essential requirement. Therefore, we introduce a systematic characterization of CPU load, GPU utilization, RAM consumption, power draw, throughput, and thermal behaviour of TensorRT-optimized YOLOv10s, YOLOv11s and YOLO2026n pipelines running on NVIDIA Jetson Nano under a large-scale fault injection campaign targeting both lane-following and ob ject detection tasks. Faults are synthesized using a decoupled framework that leverages large language models (LLMs) and latent diffusion models (LDMs), based on original data from our JetBot platform data collection. Results show that across both tasks and both models the inference engines keep GPU occupancy stable, temperature rise under control, and power consumption within safe limits, while memory usage settles into a consistent release pattern after the initial warm-up phase. Object detection tends to show somewhat more variability in memory and thermal behavior, yet both tasks point to the same conclusion: the TensorRT pipelines hold up well even when the input data is heavily degraded. These findings offer a hardware-level view of model reliability that sits alongside, rather than against, the broader body of work focused on inference performance at the edge.

Problem

• edge object detection
• fault injection
• hardware utilization
• inference performance
• resource-constrained platforms

Innovation

• fault injection
• edge object detection
• TensorRT optimization
• hardware reliability
• LLM-LDM fault synthesis