There are many ways that machine learning can help improve our FPGA designs, but when it comes to quality of results and design iteration – it’s a game changer. In this episode of Chalk Talk, Amelia Dalton chats with Nick Ni from Xilinx about the benefits of machine learning design optimization, what hierarchical module-based compilation brings to the table, and why extending a module design into an end-to-end flow can make all the difference in your next FPGA design.

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