2D Winograd CNN Chip for COVID-19 and Pneumonia Detection

Abstract

In this paper, a two-dimensional Winograd CNN (Convolutional Neural Network) chip for COVID-19 and pneumonia detection is proposed. In light of the COVID-19 pandemic, many studies have led to a dramatic increase in the effects of the virus on the lungs. Some studies have also pointed out that the clinical application of deep learning in the medical field is also increasing, and it is also pointed out that the radiation impact of CT exposure is more serious than that of X-ray films and that CT exposure is not suitable for viral pneumonia. This study will analyze the results of X-rays trained using CNN architecture and convolutional using Winograd. This research will also set up a popular model architecture to realize four kinds of grayscale image prediction to verify the actual prediction effect on this data. The experimental data is mainly composed of chest X-rays of four different types of grayscales as input material. Among them, the research method of this experiment is to design the basic CNN operation structure of the chip and apply the Winograd calculus method to the convolutional operation. Finally, according to the TSMC 0.18 μm process, the actual chip is produced, and each step is verified to ensure the correctness of the circuit. The experimental results prove that the accuracy of our proposed method reaches 87.87%, and the precision reaches 88.48%. This proves that our proposed method has an excellent recognition rate.