I've just learned that the intel new BSPs support streaming data from host to FPGAs and vice-versa over the PCIe. I'm still wondering what are the use cases or specific applications that can benefit from such feature! Is there any specific scenario, either in Deep Learning, Reinforcement Learning, Big-Data, or Streaming Processing that can take advantage of such technology?
I found the paper below may be helpful for you to further understanding the streaming interface:
Regards -SK Lim
To answer the original question, this feature is very useful for "out-of-core processing"; i.e. processing data that is too big to fit on the FPGA external memory but can fit on the host memory. There is a large body of work in HPC and Big Data using GPUs where overlapping/pipelining of compute and PCI-E transfer is implemented using double buffering on the GPU memory. For applications that can be "streamed", host channels on FPGAs can be used to efficiently implement out-of-core processing without the need for double-buffering. However, for applications that cannot be streamed, this feature is not applicable and double buffering will have to be used as is done on GPUs.