Why run AI on an FPGA?
FPGAs are a strong option when inference must be deterministic, close to the sensor, and within a limited power budget.
- Predictable latency. A dedicated data path avoids batching, OS scheduling jitter, and PCIe transfers.
- Efficient edge inference. The hardware implements the operations and data movement required by the model, reducing unnecessary work and memory traffic.
- Direct sensor integration. Inference can sit inside the image pipeline, from MIPI CSI-2 input through preprocessing and decision output, without a host CPU in the critical path.
- Field updates. Quantized models and accelerator logic can be updated on the same FPGA as requirements change.
If a GPU or NPU is a better fit for the workload, we’ll recommend it. The initial assessment is free.
What we deliver
AI vision: detection, segmentation, classification
Our core specialty. We deploy YOLO-family object detectors and similar single-shot models (SSD, RetinaNet), segmentation networks (U-Net, DeepLab), classification backbones (ResNet, MobileNet, EfficientNet), and keypoint/pose networks, quantized and compiled for FPGA fabric. The deliverable is an end-to-end camera-to-decision pipeline: sensor interface (MIPI CSI-2, GigE Vision, CoaXPress), custom ISP and preprocessing, in-fabric inference, and post-processing (NMS, tracking hooks) — with fixed, measured latency from sensor to result, for machine-vision, broadcast, medical, and robotics applications.
Model-to-fabric deployment
Quantization, pruning, and compilation of your trained networks (CNNs, vision transformers, custom architectures) onto AMD/Xilinx, Intel/Altera, Lattice, Microchip, and Efinix devices — using Vitis AI, FINN, hls4ml, or custom RTL for operators and data paths that need further optimization.
LLM & transformer acceleration at the edge
Deployment of compact language and transformer models into FPGA fabric for on-device, air-gapped, or latency-critical applications.
AI accelerator architecture & feasibility
Benchmarking, resource estimation, and architecture trade studies covering device selection, throughput, latency, power, and fabric utilization.
Verification & delivery
Delivery includes RTL verification, timing closure, board bring-up, and measurements from the target hardware.
Why bard0
- AI-assisted RTL development. Our internal design flow includes the open-source fpgaZero toolchain for RTL iteration and review.
- Camera-interface experience. Our IP portfolio includes the MIPI Aggregator plus GigE Vision and CoaXPress cores, so we can design the sensor interface and inference pipeline together.
- Multi-vendor experience. We work with AMD/Xilinx, Intel/Altera, Lattice, Microchip, and Efinix, and select the device against the project requirements.
Frequently asked questions
Can you run YOLO on an FPGA?
Yes — this is the workload we deploy most. Quantized YOLO variants run on FPGAs through Vitis AI (DPU overlays), FINN, or custom accelerators, with the practical trade being model size vs. resolution vs. frame rate on the chosen device. Smaller variants fit mid-range parts at real-time frame rates; larger models or higher resolutions push toward Versal AI Edge-class devices. A feasibility study gives you the numbers for your model and camera setup.
Which vision models run well on an FPGA?
Quantization-friendly CNNs map best: YOLO variants, SSD, and RetinaNet for detection; ResNet, MobileNet, and EfficientNet for classification; U-Net and DeepLab for segmentation. Vision transformers (ViT, DETR-style models) are feasible on larger devices but cost more resources per frame. If your model is custom, the feasibility study tells you how it maps before any porting work starts.
Is an FPGA faster than a GPU for AI inference?
FPGAs can reduce latency for single-sample edge inference because the data path is built around the model and does not depend on batching. GPUs are generally better suited to high-throughput datacenter workloads. We benchmark the model and target hardware to determine which architecture fits.
Can you run an LLM on an FPGA?
Compact, quantized language and transformer models can run on an FPGA. This is most practical for task-specific models with strict latency, privacy, or power requirements; frontier-scale models require other hardware.
Which FPGA is best for AI?
It depends on your model size, throughput, and power budget — from Lattice for milliwatt edge vision to AMD Versal AI Edge for heavy pipelines. Our feasibility study answers this with numbers for your workload.
What frameworks do you support?
PyTorch, TensorFlow, and ONNX as input; Vitis AI, FINN, hls4ml, or custom RTL as the path to fabric.
How long does an FPGA AI project take?
Feasibility in 1–2 weeks; a working prototype typically in 6–12 weeks depending on model complexity and hardware integration.
Send us a model summary, target latency, power budget, and interface requirements. We’ll assess device options, expected performance, implementation effort, and cost.