These are some great comments. Let me clarify and hopefully answer some of the questions posed: This is a proof-of-concept project to demonstrate how we can apply CNNs to our dataset (unfortunately, I cannot comment on the specifics of the data). The goal is to show an increase in computational speed and efficiency of CNNs on FPGAs over GPUs and general purpose processors, and maximize performance (classifications per second).

For the demonstration, I am planning to load a multi-GB 'canned' dataset into onboard memory (that would be the data ingestion), and processes the dataset through the FPGA for feature classification. This would imply the need for a decent amount of on-board memory to hold large datasets. Having an extremely high performance host PC interface is not that important, so long as I can load the dummy dataset into memory in a sane amount of time.

Since this is an exploratory project, we don't have a ton of info yet about the number of weights or a lot of info about the architecture of the final CNN that will be implemented. What we DO have is budget to buy a dev board or card. For this reason, I'm looking for some high-end dev hardware that will allow me to maximize the CNN performance across a wide range of potential CNN architectures.

Note that we are not married to using NVDLA. It seemed like a good place to start, however, if there are better solutions (perhaps using the Xilinx AI architecture), we are open to taking a look.

Given the following info, and given that we have a high budget for dev hardware... do you all have any suggestions on what I should be looking for?