Eltwise_mult with broadcasting on FPGA

Hi,

I’m working with a customized model, and one of the steps involves applying a mask with shape [1, 1, H, W] to crossing 96 channels of a feature-level tensor of shape [1, 96, H, W], which is the output of a convolution layer. In PyTorch, this can be done using torch.mul(tensor1, tensor2) where broadcasting handles the channel mismatch and performs element-wise multiplication.

Is there a way to make this operation work directly on an FPGA? I want the entire model computation to be executed on the FPGA without involving the CPU. It seems to me that the "enable_eltwise_mult: true" not perform broadcasting that handles the channel mismatch. I may be wrong.

Thanks!

Hi @JohnT_Intel

Some AGX7_**.arch files are included with the FPGA AI Suite 2025.1. In some of these .arch files, the option "enable_eltwise_mult: true" is defined, while in others it is not.

When "enable_eltwise_mult" is set to "true", the FPGA supports element-wise multiplication for tensors with matching shapes, such as [1, 1, H, W] × [1, 1, H, W]. However, it does not support broadcasting—for example, multiplying tensors of shapes [1, 1, H, W] × [1, channel, H, W] is not supported on the FPGA. This is what I meant by "enable_eltwise_mult : true" is not working.

As for the FPGA AI Suite license, I believe it refers to the Quartus Pro license required for building custom bitstreams (.sof files). I do not have a Quartus Pro 2025 license, but I do have a license for Quartus Pro 2021, which I believe was provided by Terasic with the DE10-Agilex development kit.

Thanks.