FPGA implementation of an sEMG classifier
This master’s thesis discusses the implementation of a convolutional neural network on a Field Programmable Gate Array (FPGA). It deals with implementation be describing a tool chain, starting with the designing of a model in Keras, transforming the model to Hardware Descriptive Language, and finally implementing it on an FPGA. Performance on three different scales of the same model topology are c