When selecting an FPGA kit for deploying deep learning networks, several factors such as performance, ease of use, support for deep learning frameworks, and available resources should be considered.

Srinivas Talasila

Good Choices:

• Xilinx Zynq UltraScale+ MPSoC: Popular, powerful, good software support. (Cost:1000−1000-1000− 5000+)
• Intel Arria 10/Cyclone 10: Another strong option, good for performance and lower cost versions available. (Cost: $500 - $3000+)

Key Considerations:

• On-Chip RAM: Important for model storage.
• DSP Slices: Crucial for computations.
• Software Support: Look for frameworks (Vitis/OpenVINO).
• Budget: Costs vary widely.

General Advice:

• Start with a kit that matches your budget and performance needs.
• Check community forums for specific model compatibility.