Implementation of a Convolutional Neural Network Into an Embedded Device for Polyps Detection.

The increasing rates of colorectal cancer and associated mortality have attracted interest in the use of computer-aided diagnosis tools based on artificial intelligence (AI) for the detection of polyps at an early stage. Most AI models are implemented on software platforms; however, due to the demands of embedded devices, hardware implementations have to fulfill the demands of real-time applications with better accuracy and low-power consumption. In this letter, we propose an optimized four-layer network that can be implanted into an embedded device and determine the feasibility of implanting our convolutional neural network (CNN) into a microprocessor. The essential functions of the CNN (i.e., padding, convolution, ReLU, max-pooling, fully connected, and softmax layers) are implemented in the microprocessor. The proposed method achieves efficient classification with high performance and takes only 2.5488 mW at a working frequency of 8 MHz. We conclude this letter with a discussion of the results and future direction of research.