Common Mistakes in FPGA ML Accelerator Design (and How We Avoid Them)
FPGA-based ML acceleration looks straightforward on paper: implement convolution, add parallel MAC units, stream data, and achieve high TOPS/W. But once a design moves beyond small demos and begins running real models at real resolutions, the engineering challenges shift dramatically. The bottleneck is rarely compute alone. Instead, it becomes