Unable to map lookup tables to RAM in HDL coder

I am trying to reduce the utilization of lookup tables in my HDL code while using the Native Floating point library so, I enabled the option to 'map lookup tables to RAM' in the optimization settings. But even after selecting a synthesis tool, the RAM utilization is still at zero. Is there something I am missing that is not mentioned in the documentation?

Answer 1

[Kiran Kintali]

Can you share a sample model with your configuration settings and desired synthesis results?

All floating point operator level customization options are available in the floating point panel or right-click HDL block options.

Currently 'Map lookup tables to RAM' is not plugged into the floating point operator specific customizations. I will communicate this request to the team.

Please review some sample synthesis metrics here Synthesis Benchmark of Common Native Floating Point Operators. You can easily regenerate the latest synthesis results using this example for your choice of synthesis tool, data types, frequency, fpga device, power settings.

[Ishita Ray]

Thanks for your response, Kiran. I unfortunately cannot share the model due to its large size and our IP policy but here are the current synthesis results:

The resource utilization was not an issue when I was using fixed point but switching to floating point made the code LUT-heavy. I have also attached the configuration settings. Please let me know if you find any error there and I'll review the document you shared.

Also, could you clarify what you mean by "Currently 'Map lookup tables to RAM' is not plugged into the floating point operator specific customizations"? Is the option of mapping lookup tables to RAM not available when using native floating point or are you saying that that option is not specific to floating point (which is fine)?

[Kiran Kintali]

The Map loopup tables to RAM option currently applies to Lookup blocks in Simulink and does not apply to implicit luts inferred within Native Floating Point.

In general use of floating-point incurs additional resources. For high dynamic range applications specifically designs where fixed-point error accumulates rapidly within feedback loops HDL Coder native floating point features are recommended.

Feel free to reach out to tech support for additional assistance. In addition to mapping to RAMs, there are other streaming, sharing and pipelining optimizations that you can employ for futher reducing the size of the hardware and improving performance of the generated code.

[Ishita Ray]

Okay, I understand now. My model does have lookup table blocks in it so I was surprised that none of those were mapped to RAM either. I see no RAM utilization in the systhesis report neither do is there any file generated for highlighting the lookup tables converted to RAM. I'm trying to figure out if there's something is missing.