Hi Paul,
When encountering the error related to insufficient InputMemorySize for the Conv module in the processor configuration, it is crucial to adjust the memory size appropriately to accommodate the layers in your custom CNN network. The error message specifically mentions that the InputMemorySize for the Conv module needs to be increased to [171 171 7] or more to deploy the 'AP1' layer successfully.
To address this issue, follow these steps to adjust the InputMemorySize for the Conv module using the dlhdl.ProcessorConfig object:
Set InputMemorySize for Conv Module: Use the setModuleProperty method of the ProcessorConfig object to set the InputMemorySize for the Conv module to [171 171 7]:
hPC.setModuleProperty('conv', 'InputMemorySize', [171 171 7]);
Verify Configuration: After setting the InputMemorySize, ensure that the configuration is updated correctly by checking the properties of the ProcessorConfig object:
disp(hPC.ModuleProperties);
Re-Estimate Performance: Once you have adjusted the InputMemorySize, attempt to estimate the performance of the CNN network again to verify if the error persists:
hPC.estimatePerformance('Network', customCNN);
Iterative Adjustment: If the error persists with higher requirements for subsequent layers like the 2D Average Pooling layer, repeat the process of increasing the InputMemorySize for the respective modules until all layers can be accommodated.
By following these steps and iteratively adjusting the InputMemorySize for the modules in your custom CNN network, you should be able to resolve the issue of insufficient memory size for deploying the layers successfully. Remember to validate the configuration changes and re-estimate the performance after each adjustment to ensure compatibility with your network architecture.
If you encounter any further challenges or require additional assistance, feel free to provide more details for a more tailored solution. Good luck with optimizing your deep learning accelerator on Xilinx FPGA!
