The edge AI inference conversation tends to orbit the same few options: NVIDIA Jetson for serious workloads, NPU-equipped SoCs for ultra-low-power sensing, cloud offload for anything too heavy. FPGAs rarely enter the discussion unless someone on the team already has a background in hardware design. That’s a gap worth closing, because FPGAs are uniquely capable for a specific class of edge AI problems — and that class is larger than most software-first teams realise.
What Makes FPGAs Different
An FPGA is an array of reconfigurable logic blocks that you configure to implement any digital circuit you want. Unlike a CPU or GPU, which execute instructions sequentially or in parallel through a fixed processing pipeline, an FPGA implements a custom circuit. The computation happens in hardware, not in software running on hardware.
For AI inference, this means a few things. First, the pipeline can be tailored exactly to your model’s architecture — no wasted cycles executing general-purpose instructions that don’t apply to your specific neural network. Second, the data path is fixed and deterministic: inference latency has a hard upper bound, not a statistical average. Third, power consumption is often dramatically lower than GPU alternatives because you’re only implementing the logic you need.
The tradeoff is development complexity. Programming an FPGA requires either traditional hardware description languages (VHDL, Verilog) or high-level synthesis tools that compile C/C++ or Python-like languages to hardware. It’s not as fast to iterate as writing PyTorch and deploying to a Jetson.
The Cases Where FPGAs Win
Hard real-time constraints. If your edge AI application has a latency requirement measured in microseconds — not milliseconds — FPGAs are almost always the right choice. Industrial motor control, optical inspection at production line speeds, high-frequency radar signal processing: these applications require inference results before the next sample arrives. GPU inference latency, even on optimised hardware, has statistical variance. FPGA inference latency is deterministic. You can guarantee that the result will be available within N microseconds, every time.
Custom data types and quantisation. FPGAs let you implement inference in arbitrary numeric precision. Standard hardware runs 8-bit or 16-bit integer operations at best; FPGAs can implement 4-bit, 6-bit, or even 2-bit inference pipelines for specific model architectures where the accuracy tradeoff is acceptable. For workloads where power is constrained and model size is small, custom precision inference on FPGA can deliver better results per milliwatt than any fixed-precision chip.
Multi-sensor sensor fusion. When you have ten camera inputs, each needing preprocessing and inference, feeding them all through a single CPU or GPU creates pipeline bottlenecks and latency variance. An FPGA can implement parallel inference pipelines for each sensor simultaneously, all running at the same deterministic rate.
Regulated environments with strict change control. In medical devices and aerospace, hardware changes go through lengthy validation processes. An FPGA that’s been validated can have its bitstream updated — new model, new preprocessing logic — without triggering a full hardware re-validation in some regulatory frameworks. This is a significant advantage when you need to update model versions in deployed devices.
The Main Players
AMD (Xilinx). The Versal AI series and the older Zynq UltraScale+ families are the most commonly deployed FPGAs for edge AI. AMD’s Vitis AI toolchain compiles quantised neural networks from PyTorch, TensorFlow, and ONNX to hardware implementations running on Xilinx FPGAs, which substantially lowers the barrier compared to pure HDL development.
Intel (Altera). The Agilex and Stratix series target high-performance applications; the Cyclone and MAX families are lower-cost options for simpler inference tasks. Intel’s OpenVINO toolkit has FPGA backend support, and the Open FPGA Stack makes it easier to build production AI accelerator designs on Intel FPGAs.
Microchip Technology (formerly Actel). PolarFire FPGAs occupy an interesting niche — moderate performance with very low static power consumption, making them suitable for always-on inference in battery-powered or energy-harvesting edge devices. PolarFire’s power profile is competitive with NPU-equipped microcontrollers for small model inference.
Lattice Semiconductor. The Lattice sensAI solution targets ultra-low-power FPGA inference on their ECP5 and MachXO families. This positions them for TinyML applications where conventional NPUs are too large or power-hungry, occupying a niche between microcontroller-class NPUs and mid-range FPGAs.
The Practical Barrier: Toolchain
The honest answer to “why don’t more edge AI projects use FPGAs” is the toolchain. Vitis AI, Intel’s OpenVINO FPGA backend, and similar high-level synthesis tools have improved dramatically, but there’s still a meaningful skill gap between deploying a model to a Jetson Orin and deploying it to an AMD Versal.
The path forward for teams without hardware design backgrounds is usually high-level synthesis: write the inference logic in C++ or use a tool like AMD’s Vitis AI to compile a pre-trained quantised model to FPGA bitstream with minimal HDL exposure. For standard model architectures (ResNet, YOLO variants, MobileNet), this mostly works. For custom architectures or highly optimised custom layers, you’ll need someone who can write hardware.
Choosing Between FPGA, NPU, and GPU
The decision usually comes down to three questions:
- Do you need deterministic latency under 1ms? FPGA.
- Is power the primary constraint with a well-defined small model? Evaluate NPU SoCs first, then FPGA.
- Is development speed and model flexibility the priority? GPU (Jetson or similar).
FPGAs don’t win every evaluation. For most computer vision applications on standard model architectures at tens of milliseconds latency tolerance, an NPU or Jetson is faster to deploy and good enough. The cases where FPGA is clearly the right answer are real and common in industrial settings — they’re just not the typical greenfield IoT project.