An Embedded System for Classification and Dirt Detection on Surgical Instruments

Abstract: The need for automation in healthcare has been rising steadily in recent years, both to increase efficiency and for freeing educated workers from repetitive, menial, or even dangerous tasks. This thesis investigates the implementation of two pre-determined and pre-trained convolutional neural networks on an FPGA for the classification and dirt detection of surgical instruments in a robotics application. A good background on the inner workings and history of artificial neural networks is given and expanded on in the context of convolutional neural networks. The Winograd algorithm for computing convolutional operations is presented as a method for increasing the computational performance of convolutional neural networks. A selection of development platform and toolchains is then made. A high-level design of the overall system is explained, before details of the high-level synthesis implementation of the dirt detection convolutional neural network are shown. Measurements are then made on the performance of the high-level synthesis implementation of the various blocks needed for convolutional neural networks. The main convolutional kernel is implemented both by using the Winograd algorithm and the naive convolution algorithm and comparisons are made. Finally, measurements on the overall performance of the end-to-end system are made and conclusions are drawn. The final product of the project gives a good basis for further work in implementing a complete system to handle this functionality in a manner that is both efficient in power and low in latency. Such a system would utilize the different strengths of general-purpose sequential processing and the parallelism of an FPGA and tie those together in a single system.