## 1 Introduction  
Diabetic retinopathy (DR) is caused by damage to the blood vessels in the tissue at the back of the eye (retina) causing vision impairment and blindness. Uncontrolled blood sugar is a major risk factor. Since diabetic retinopathy lacks early symptoms, it is very difficult to detect the diseases at an early stage [1]. The 2020 diabetes report from the Center for Disease Control (CDC) found that the US diabetic population rose to 9.4% or 3.3 million people [2]. Another 88 million people, more than 1 out of 3 adults, have pre-diabetes. When left untreated, pre-diabetes can lead to type-2 diabetes within five years. The diabetic population worldwide was more than 250 million in 2017 [3]. This number is projected to rise to around 629 million by 2045 [4]. An estimated one-third of the diabetic population has diabetic retinopathy symptoms, with a significant portion of them being vision-threatening [5]. Currently, 7.7 million Americans are impacted by this disease, and it is expected to rise to 11.3 million by 2030 [6]. One of the major contributors of increased diabetic retinopathy-related vision loss is the lack of access to the medical care needed to catch the disease at an early stage.  
The current diabetic retinopathy detection method involves a dilated eye exam. In this exam, eye-dilating drops placed inside a patient's eye widen eye pupils and allow doctors to see the eye blood vessels [7, 8]. A special dye is injected, and pictures are taken as the dye circulates through blood vessels. The images are used to further examine the blood vessels and catch any damaged veins or fluid leaks. These eye exams are very effective; however, for patients without health insurance, it costs $200 or more in the US and is often unavailable in remote or developing parts of the world [9, 10].  
The detection of diabetic retinopathy using computer vision has recently been considered as a possible alternative to a visual analysis by a clinician. One such example is the Kaggle competition on diabetic retinopathy, in which more than 600 teams participated [11]. Similarly, [PERSON] et al. present an automated detection of diabetic retinopathy using TensorFlow and GoogLeNet in [12]. Different aspects of pre-processing to feature extraction are presented. In [13], [PERSON] et al. presents the classification of diabetic retinopathy using convolution neural network. Using a self-gated soft-attention mechanism and pre-trained coarse network, the presented work performs four-class hierarchical classification based on the severity of the disease. The detection of diabetic retinopathy using deep learning is also presented in [14, 15, 16, 17]. In another approach, hardware-based solutions have also been reported [18]. With a digital signal processing kit developed by Texas Instrument, the article reports the possibility of using hardware for detecting diabetic retinopathy. These studies were conducted using high-resolution retina images taken with a fundus camera. Efforts to detect retinopathy from images taken with a low-cost camera have also been reported [19].  
This manuscript furthers the reported work through a solution toward detecting diabetes retinopathy using embedded computer vision for implementation on a cost-effective portable NVIDIA Jetson TX2 hardware [20] and on-device real-time classification without the need for an Internet connection. We present training and testing of the Caffe and Keras model using healthy and diabetic retinopathy images. The training and testing set consists of 83,000 high-resolution retina images labeled by experts. The Caffe model was trained and tested on NVIDIA-embedded hardware.