Signal processing and neural network implementations on field programmable gate arrays (FPGA); FPGA designs of digital filters, Fourier transform, JPEG decoding, fast convolution, Kalman filter and Viterbi decoding; circuit design techniques commonly used in signal processing and neural network, such as pipelining, parallel processing, folding, unfolding and systolic array. Prerequisites: Grade of C or better in ECEN 248 and ECEN 314; junior or senior classification Credits 4. 3 Lecture Hours. 2 Lab Hours.
Signal processing and neural network implementations on field programmable gate arrays (FPGA); FPGA designs of digital filters, Fourier transform, JPEG decoding, fast convolution, Kalman filter and Viterbi decoding; circuit design techniques commonly used in signal processing and neural network, such as pipelining, parallel processing, folding, unfolding and systolic array. Prerequisites: Grade of C or better in ECEN 248 and ECEN 314; junior or senior classification Credits 4. 3 Lecture Hours. 2 Lab Hours.
