The proliferation of complex deep learning (DL) models has revolutionized
various applications, including computer vision-based solutions, prompting
their integration into real-time systems. However, the resource-intensive
nature of these models poses challenges for deployment on low-computational
power and low-memory devices, like embedded and edge devices. This work
empirically investigates the optimization of such complex DL models to analyze
their functionality on an embedded device, particularly on the NVIDIA Jetson
Nano. It evaluates the effectiveness of the optimized models in terms of their
inference speed for image classification and video action detection. The
experimental results reveal that, on average, optimized models exhibit a 16.11%
speed improvement over their non-optimized counterparts. This not only
emphasizes the critical need to consider hardware constraints and environmental
sustainability in model development and deployment but also underscores the
pivotal role of model optimization in enabling the widespread deployment of
AI-assisted technologies on resource-constrained computational systems. It also
serves as proof that prioritizing hardware-specific model optimization leads to
efficient and scalable solutions that substantially decrease energy consumption
and carbon footprint.

研究了在低计算能力和低内存设备上部署复杂的深度学习模型的优化方法以提高推断速度，证明了硬件特定的模型优化能够有效降低能源消耗和碳足迹。