An Energy-Efficient and Reconfigurable CNN Accelerator Applied To Lung Cancer Detection

Abstract

We propose a system to fast and easily detect lung cancer by breathing into the device, which is not invasive. Some particular substances only exist in lung cancer patients' breathing. Based on this, we use the CNN model to extract the feature in the gas exhaled by the testee. Then, the neural network will give out the prediction of lung cancer. To accelerate the computation of CNN, we design a hardware accelerator and implement it with FPGA (Field Programmable Gate Array). By comparing the performance, like power consumption and energy efficiency of different architectures, we could find the most appropriate architecture for us. Ultimately, we could reduce memory access by about 20% and reduce 12% of the energy consumption, achieving low power at edge devices. The performance of the CNN model is with a training accuracy 88.41%, a testing accuracy 85.29%, a false negative rate 5.8%, and a false positive rate 41.17%