If you click on Papers, there is a link to "Going Deeper with Embedded FPGA for Convolutional Neural Network", which compares against the TK1: https://nicsefc.e

If you click on Papers, there is a link to "Going Deeper with Embedded FPGA for Convolutional Neural Network", which compares against the TK1: https://nicsefc.ee.tsinghua.edu.cn/media/publications/2016/FPGA2016_None_6tAJnDW.pdf https://nicsefc.ee.tsinghua.edu.cn/media/publications/2016/F... While not the TX1 vs FPGA result you want, this is very close. For example they aren't using the latest FPGA or GPU, and are not using TensorRT on the GPU and on the FPGA side they are using fatty 16-bit weights on an older FPGA rather than newer stuff you can do with lower precision (which improves the efficiency of the FPGA having more high speed RAM collocated with computation vs GPU which is primarily off-chip). If you want to learn more about this stuff, I suggest a presentation by one of Bill Dally's students (chief scientist at NVIDIA): http://on-demand.gputechconf.com/gtc/2016/presentation/s6561-song-han-deep-compression.pdf http://on-demand.gputechconf.com/gtc/2016/presentation/s6561...