Low Power FPGA Hardware for Machine Learning

Course Description

This course provides comprehensive coverage of both practical and theoretical aspects essential for designing digital systems with high speed and energy efficiency, with a specific focus on machine learning. The emphasis is placed on implementing designs for reconfigurable architectures like FPGA and conducting real-world testing of machine learning systems using an FPGA development board. Various topics will be covered, including hardware architectures, fixed-point implementation, pipelining, optimized synthesis, and routing techniques aimed at enhancing performance while reducing hardware size and power consumption. The course consists of four homework and concludes with a final project that requires hardware design using Verilog, along with evaluation through simulation and FPGA hardware. Tools to be used: Xilinx Vivado, FPGAs: Artix FPGA

Code

EN.520.643

Credits

3

Subject

Electrical & Computer Engineering