## 1 Introduction  
With the development of information and communication technology, various fields such as big data, Internet of Things (IoT), and AI are developing [1]. Machine learning (ML), related to AI, is a technology that computers can learn on their own to create and predict models. Furthermore, deep learning is a field of machine learning using deep neural network theory, using the principle of neural network corresponding to the human brain [2]. The fields of deep learning can be largely divided into image classification, object detection, natural language processing, and voice/speech recognition [3]. In particular, object detection can be divided into one-stage detectors and two-stage detectors, in which a one-stage detector has a YOLO, SSD-based model, and a two-stage detector has an R-CNN-based model. Among them, YOLO is a one-stage detector model that is very fast with a simple processing process, but has relatively low accuracy for small objects [4].  
Deep learning can make inferences through computation on the CPU, but it requires GPU hardware for performance benefits. To this end, Nvidia embedded a core called Computed Unified Device Architecture (CUDA) in the GPU, which can be calculated faster than the CPU by utilizing GPUs during deep learning. Therefore, it is possible to perform computations on the GPU by using the CUDA Cores located in the GPU, so that the learning and result inference required for object detection using deep learning can be performed more efficiently [5,6].  
Recently, as IoT and embedded environments are in the spotlight, not only are deep learning-related studies being conducted in desktop-like environments, but deep learning-related studies in embedded environments are also continuously being conducted. The Jetson platforms sold by Nvidia as embedded systems are small modules, very small compared to desktops, with slightly less performance and accuracy. However, they have good power efficiency and provide at least four times higher performance when compared to CUDA cores, through built-in cores called Tensor Cores. In addition, TensorFlow and PyTorch are representative frameworks used for deep learning-based object detection. Each deep learning framework can operate in various programming languages such as C++, Java, and Python, so you can use it easily even if you are not familiar with a specific programming language.  
Therefore, CUDA and Cuda Deep Neural Network Library (cuDNN) were installed using the JetPack package into AGX Xavier, one of the embedded systems provided by Nvidia. It then compared TensorFlow running on CUDA Cores with TRT running on Tensor Cores. The model used for comparison is YOLOv4, the dataset used COCO and PASCAL VOC, and the source code implemented to operate in TensorFlow was used. In TensorFlow, it was converted to TF-Lite, which operates on a mobile basis, and TF-TRT, which uses Tensor Cores. In addition, various frameworks were prepared by applying Mixed Precision's float32 and float16 in the converted deep learning framework and basic TRT (pure TensorRT that does not use TensorFlow). Experiments were conducted to quickly infer objects, even at the risk of some performance degradation when compared to desktop environments in embedded systems.  
Section 2 of this paper examines the YOLO model used for deep learning-based object detection and introduces Nvidia Jetson platforms. Section 3 examines cases of deep learning in embedded systems and similar research cases comparing deep learning frameworks in various environments. Section 4 introduces some modified source codes for architecture comparison analysis and performance inference measurements for the deep learning framework. Section 5 compares CPU utilization, GPU utilization, object accuracy, latency, and power consumption through performance evaluation comparisons using the modified deep learning framework. Section 6 concludes this paper with conclusions.