I am working on a high-frequency trading (HFT) application where every microsecond counts. Standard GPU inference has too much jitter and "batching" delay for our needs. I've decided to move our decision-making model to an AMD (Xilinx) Alveo board using FPGA-based acceleration.
I’m specifically looking for advice on the best entry point: should I use high-level tools like Vitis AI to compile my PyTorch model directly, or will I get better performance by hand-coding the data path in Verilog/VHDL? Also, how do you handle the fixed-point math requirements? Is INT8 quantization enough to maintain accuracy for complex models, or should I be looking at custom 12-bit or 14-bit types?
3 answers
Kimberly is right—RTL is the "nuclear option" for performance. But regardless of your flow, the biggest challenge you'll face is Quantization. FPGAs are incredibly fast at integer math but struggle with the floating-point numbers (FP32) typically used in AI training.
Implementing AI on an FPGA is fundamentally different from a GPU. While a GPU uses a fixed architecture to process data in massive batches, an FPGA allows you to build a fully custom pipeline where data flows through the silicon like water through a series of pipes.
For most teams in 2026, the best approach is the HLS (High-Level Synthesis) Flow. Using tools like Vitis AI or Intel’s FPGA AI Suite, you can take a trained model from PyTorch/TensorFlow and "compile" it into hardware. The tool handles the mapping of neural network layers to DSP slices and Block RAM (BRAM). However, if you need that sub-microsecond edge for HFT, you might need an RTL (Register Transfer Level) Flow, where you manually optimize the memory hierarchy to eliminate every single clock cycle of latency.
Don't forget about Data Movement. The bottleneck is rarely the math; it's usually moving data from the PCIe bus or network interface into the FPGA's on-chip memory.
Exactly, Steven. In a custom FPGA design, you can implement Inline Processing. If your data comes in via a 100G Ethernet core, the FPGA can start the first layer of inference as the packet is still arriving. This "streaming" capability is why FPGAs can achieve "Inference-under-10µs" while GPUs are still waiting for the memory buffer to fill up. If you're using Xilinx, look into the DPU (Deep Learning Processing Unit) IP cores—they are highly optimized "programmable" engines that give you a middle ground between flexibility and raw RTL speed.
To get the most out of your FPGA, you must convert your model to Fixed-Point (e.g., INT8). This process, called quantization, can reduce your memory and power usage by up to 75%. In 2026, we’re even seeing Mixed-Precision designs where critical layers use 16-bit precision while others use 4-bit (INT4) to save space. I highly recommend using Quantization-Aware Training (QAT) rather than just converting the model after training; this allows the model to "learn" how to handle the rounding errors, keeping your accuracy nearly identical to the original FP32 model.