What We Do
Several porting paths, one accuracy guarantee.
☁️
Cloud or GPU → On-Device (De-Cloudify)
Remove the cloud inference dependency entirely. We compress, quantize, and deploy your model to an on-device compute platform — FPGA or NPU — eliminating cloud latency, connectivity risk, and per-inference cost. Your system makes decisions locally, in real time, regardless of network availability.
🖥️
GPU Server → FPGA Pipeline
For applications that need deterministic sub-millisecond latency — which GPU scheduling cannot guarantee — we restructure your model for FPGA deployment via Vitis AI HLS synthesis. Integrated with our CameraLink IP for camera-direct pipelines.
🤖
PyTorch / TensorFlow → AMD NPU
We export your model to ONNX, apply INT8 or INT4 quantization, develop MLIR-AIE kernels for the AMD Ryzen AI / XDNA NPU, and deploy via ONNX Runtime on-device. Validated for latency and accuracy before handover.
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FPGA Generation Migration
Moving from one Xilinx or AMD FPGA generation to another — or from one board family to another. RTL porting, IP re-synthesis, driver update, BSP revalidation. We carry across the full stack, not just the bitstream.
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Cross-NPU Platform Migration
Model running on one NPU target that needs to move to another — Intel, Qualcomm, Rockchip, or AMD. ONNX conversion, re-quantization, target-specific kernel tuning, and accuracy validation against the source model.
⚖️
Edge + Cloud Split Deployment
For applications where full on-device deployment isn't feasible — we partition the model at the right layer boundary. Inference-critical layers run on-device; heavier analytics run in the cloud. Clean interface between the two, with no cloud dependency for the real-time decision path.
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Accuracy Regression Validation
Every porting engagement includes formal accuracy benchmarking against a held-out production test set. We prove the ported model matches the source model on the metrics that matter — and deliver a signed-off accuracy report alongside the ported model.
Who This Is For
Things we do well.
Companies whose cloud inference costs have become unsustainable at production volume.
Teams running GPU server inference that can't meet the latency requirements of their production line.
Organisations with data sovereignty or air-gap requirements that prevent cloud inference.
AI teams who have a trained model and need a hardware deployment partner with FPGA and NPU depth.
What You Get
A validated model. On the right hardware.
Validated Model on Target Hardware
A validated model running on the target hardware platform — FPGA, NPU, or on-device runtime — optimised for the latency, power, and cost constraints of your production environment.
Formal Accuracy Equivalence Report
A signed-off accuracy report proving the ported model matches the original on the metrics that matter. Every engagement closes with documented proof.
Deployment Documentation & Integration Guidance
Inference benchmarks, deployment documentation, and integration guidance — everything your team needs to take the ported model cleanly into production.
Get Started
Need your model on better hardware?
Need your model running on better hardware — without trading away accuracy? Tell us your current deployment environment and target platform.
Last updated: March 2026