I did a quick pass-over of the paper. Very interesting overview and confirming a lot of what I’ve seen! I really like FPGAs for edge applications but it gets a bit trickier in core compute.
Partial Reconfiguration (PR) takes this flexibility one step further, allowing the modification of an operating FPGA design by loading a partial configuration file. Using PR, part of the FPGA can be reconfigured at runtime without compromising the integrity of the applications running on those parts of the device that are not being reconfigured.
I didn’t even realize this was a feature. This is amazing 
FPGA programming is still much more challenging than regular software programming, and the supply of FPGA engineers is still limited.
This was my biggest concern w/r/t FPGAs moving into ROS. It is a very specific skillset and it’s just a question of manpower to be able to bring up and support them. I’ve worked on the Zynq Ultrascale platform for a couple of years and really enjoy how much effort Xilinx has put into the tools/education portion of the product. I definitely think the combination of FPGA + CPU is a game changer and will continue to attract a lot of interest from industry (which will hopefully in-turn mean more engineers)!
We’ve heard in the past that it’d be awesome to run DDS in the Programmable Logic (PL, the FPGA itself) optimizing intra-network flows while leveraging specialized communication buses for nanosecond-level intra and inter-process communications.
DDS is my first thought on what could be optimized but that exists outside of ROS so it’s likely an independent project? I’m not sure where the entry point is for that. I had some concerns about the fact that there will be limitations regarding the node count/message size. I noticed an attempt at this for ROS 1 that seemed to go well!
We are hoping to create some “open” accelerators and inspire others to contribute impacting not just ROS 2, but also Gazebo. Wouldn’t it be awesome to supercharge our simulations with GPUs?
I 100% agree that optimizations with highly parallelized compute would be awesome and very much part of the modern workflow. I have not done much work in the area but have seen plenty of proof from the computer graphics + ML worlds to know that it’s great for physics environments with lots of variables 
Note: Reposted to maintain reply linking.