@article{scholars19887,
            year = {2024},
       publisher = {Institute of Electrical and Electronics Engineers Inc.},
         journal = {IEEE Embedded Systems Letters},
           pages = {5--8},
          number = {1},
          volume = {16},
            note = {cited By 0},
             doi = {10.1109/LES.2023.3234973},
           title = {Implementation of a Convolutional Neural Network Into an Embedded Device for Polyps Detection},
        keywords = {Application programs; Computer aided diagnosis; Computer hardware; Convolution; Electric power utilization; Field programmable gate arrays (FPGA); Network layers; Neural networks, Cancer; Colorectal cancer; Convolutional neural network; Embedded device; Field programmable gate array; Field programmables; Hardware; Polyp detection; Power demands; Programmable gate array, Diseases},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147301246&doi=10.1109\%2fLES.2023.3234973&partnerID=40&md5=21ca087f3e2ccd1abc4e6f6c19d424b7},
        abstract = {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. {\^A}{\copyright} 2009-2012 IEEE.},
            issn = {19430663},
          author = {Lu, C.-K. and Liew, W. S. and Tang, T. B. and Lin, C.-H.}
}