Tell us what you need to infer, where, and how fast.
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
Model-to-fabric deployment
Quantization, pruning, and compilation of your trained networks (CNNs, 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.
Real-time vision AI pipelines
End-to-end camera-to-inference systems: sensor interface (MIPI CSI-2, GigE Vision, CoaXPress), custom ISP, and in-fabric neural network inference for broadcast, medical imaging, and machine-vision applications.
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
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.