The claim that FPGAs have better power efficiency than GPUs is a reminiscent of the past. In the real world and industry (and not in scientific papers which are written by PhDs) GPUs achieve way higher performance. The simple reason is FPGAs as devices are way behind in architecture, compute capacity and capabilities.

A very simple way to see my point is this. Check one of the largest FPGAs from Xilinx, the Alveo U280 (https://www.xilinx.com/products/boards-and-kits/alveo/u280.html#specifications). It theoretically can achieve up to 24.5 INT8 TOPs AI performance and it's a 225W card. Now check a similar architecture (in nm) embedded GPU, the AGX xavier (https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-agx-xavier/). Check the specs on the bottom. Up to 22TOPs in a 30W device. That's why FPGAs are obsolete. I have countless examples like that but you get the idea.

Don't listen to anyone saying FPGAs are better than GPUs in AI. They don't know the platforms well enough.

FPGAs are obsolete for AI (training AND inference) and there are many reasons for that. Less parallelism, less power efficiency, no scaling, they run at like 300Mhz at best, they don't have the ecosystem and support GPUs have (i.e. support for models and layers). Even the reduced precision "advantage" they had it is now gone a long time ago. GPUs can do 8bit and even FP8 now. Maybe the largest FPGA (for example a Xilinx Alveo card) can be compared with a small embedded Jetson Xavier in AI. (you can compare the performance results from each company to see yourself).

Wonder why there are no FPGAs in MLPerf? (an AI benchmark which became the standard). Yeah you guess it right. Even Xilinx realized how bad FPGAs are for AI and stopped their production for this reason. They created the new Versal series which are not even FPGAs, they are more like GPUs (specifically they work like Nvidia Tensor cores for AI).

To sum up, FPGAs are worse in everything when compared with GPUs. Throughput, latency, power efficiency, performance/cost, you name it. Simple as that.

They train faster because less memory is being used and also 16bit arithmetic is faster than 32bit