When the hot path is too slow, too power-hungry or too jittery on a CPU, it moves to fabric. FPGA accelerators sit beside the host over PCIe or CXL and run the work — AI inference, video, cryptography, genomics, line-rate packet and data processing — with deterministic latency and throughput-per-watt a CPU cannot match.
When latency must be low and deterministic, when power-per-operation matters, or when the datapath benefits from custom precision — FPGAs win. GPUs win on raw, batchable floating-point throughput. The honest answer is workload-dependent, which is why we benchmark against the current approach before recommending fabric.
Over PCIe, or CXL where coherent host memory access helps, with a DMA/shell layer moving data between host memory and on-card HBM or DDR, and the compute kernels in fabric. Feeding the kernels with enough memory bandwidth is often the real constraint, not the compute.
Both, chosen per block. HLS accelerates development for dataflow-heavy kernels; hand-written RTL is used where the last measure of performance or resource efficiency matters. The goal is the result on the card, not a methodology preference.
Tell us the specification, the constraint and the deadline.