{"id":13598,"date":"2026-08-10T11:01:34","date_gmt":"2026-08-10T11:01:34","guid":{"rendered":"https:\/\/nokobox.com\/index.php\/item\/udemy-fpga101-digital-system-design-using-verilog-and-python-2025-9\/"},"modified":"2026-08-10T11:01:34","modified_gmt":"2026-08-10T11:01:34","slug":"udemy-fpga101-digital-system-design-using-verilog-and-python-2025-9","status":"publish","type":"digital_item","link":"https:\/\/nokobox.com\/index.php\/item\/udemy-fpga101-digital-system-design-using-verilog-and-python-2025-9\/","title":{"rendered":"Udemy \u2013 FPGA101: Digital System Design using Verilog and Python 2025-9"},"content":{"rendered":"
\n

Description<\/span><\/h2>\n

FPGA101: Digital System Design using Verilog and Python. This course teaches the fundamentals of digital system design using Verilog, implementing circuits on an FPGA, and interfacing with an ARM processor. FPGAs are commonly used for computational acceleration in digital signal processing, as well as for verifying circuits before final fabrication as application-specific integrated circuits (ASICs). Focusing on the Verilog language, this course covers the fundamentals of digital circuit design, including combinational logic, sequential logic, finite state machines, and finite state machines with data paths. It also provides three additional topics on integrating digital circuitry on an FPGA with an ARM processor. At the end of the course, a final project is conducted on how to build a tensor processing unit similar to the Google TPU. In this project, participants learn the methodology for designing an accelerator for a neural network based on matrix multiplication. This methodology can be extended to other designs of interest and has wide application in engineering.<\/span><\/p>\n

What you will learn<\/span><\/h3>\n