1. They are hard to use (program). If you're a regular ML engineer, there will be a steep learning curve with Verilog/VHDL and the specifics of the chip you choose, especially if you want to squeeze all the performance out of it. For most researchers it's just not worth it. And for production deployment it's not worth the risk of investing into an unproven platform. Microsoft tried it many years ago to accelerate their search/whatever, and I think they abandoned it.
2. Cost. High performance FPGA chips are expensive. Like A100 to H100 price range. Very few people would be willing to spend this much to accelerate their DL models unless the speedup is > 2x compared to GPUs.
1. They are hard to use (program). If you're a regular ML engineer, there will be a steep learning curve with Verilog/VHDL and the specifics of the chip you choose, especially if you want to squeeze all the performance out of it. For most researchers it's just not worth it. And for production deployment it's not worth the risk of investing into an unproven platform. Microsoft tried it many years ago to accelerate their search/whatever, and I think they abandoned it.
2. Cost. High performance FPGA chips are expensive. Like A100 to H100 price range. Very few people would be willing to spend this much to accelerate their DL models unless the speedup is > 2x compared to GPUs.