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AI Acceleration
Engineering Services

Full Stack
AI Compute.

One team owns the full compute stack — from gigapixel FPGA pipelines at sub-millisecond latency, through 50–60 TOPS on-device NPU inference, to GPU-side post-processing and analytics.

AI Acceleration
Architecture

Pick the right compute platform.

The right compute substrate depends on your latency target, power envelope, and deployment environment — not a fixed architecture. We work across all three and will tell you which one fits.

FPGA
<1ms · Deterministic
Deterministic, sub-millisecond latency. High-speed inspection, camera-direct pipelines. Vivado HLS on Xilinx Zynq / Ultrascale+. Own CameraLink IP.
NPU
50–60 TOPS · No cloud
Efficient on-device AI inference without cloud. AMD Ryzen AI / XDNA — MLIR-AIE kernels, INT8/INT4 quantisation, ONNX Runtime.
GPU
Parallel · High throughput
Heavy parallel compute — post-processing, analytics, rich visualisation, and operator dashboards where throughput matters more than determinism.

Not sure which fits? We'll assess your requirements and recommend the right platform — or a combination where it genuinely makes sense.

FPGA

Core capabilities at the pixel level.

High-speed embedded vision on Xilinx Zynq and Ultrascale+ SoCs. Hardware parallelism synthesised via Vivado HLS — bridging software development and deterministic hardware execution.

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Real-Time Vision Pipelines
Sub-millisecond, deterministic latency that GPU scheduling cannot match. Ideal for high-speed inspection, multi-camera synchronisation, and real-time sorting decisions.
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Proprietary CameraLink IP
Own CameraLink IP — Base, Medium and Full mode up to 2.72 Gbps at 1–3 µs latency. Zero-copy data path. No third-party frame grabbers, no per-unit licensing cost.
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HW / SW Co-Design
FPGA fabric and ARM cores partitioned for maximum throughput on Zynq SoCs. Full BSP, kernel drivers, and application SDK written in-house — complete source handed over.
NPU

AMD Ryzen AI / XDNA.

AMD Ryzen AI / XDNA NPU — 50–60 TOPS on-die, fully on-device, independent of CPU and GPU. Three engagement tiers matched to where you are.

Architecture & Feasibility
Model analysis, ONNX integration assessment, INT8/INT4 quantisation sensitivity, XDNA AIE tile mapping, power & latency benchmarking — delivered as a go/no-go report.
Custom NPU Applications
MLIR-AIE kernel development, multi-tile compute graph design, image/signal pre-processing on AIE tiles, custom operator implementation, and Falcon Compute integration.
Validation & Enablement
CPU/GPU benchmark comparison, regression test suite, OEM qualification documentation, team handover training, and ongoing integration support.
Engineering Stack

Ownership at stack delivery.

The same five-layer design discipline applies across FPGA and NPU engagements. No black-box components in the critical path — full source at every layer.

L1
RTL / MLIR-AIE Kernel Design
Hardware logic & compute kernels
L2
IP & AIE Tile Integration
Wiring IP blocks onto the fabric
L3
Embedded Linux BSP
Board support, kernel, device tree
L4
ONNX Runtime / SDK / Drivers
Inference runtime & integration APIs
L5
Application Layer
UI, analytics, operator dashboards
Applications

Where this compute stack runs today.

Print & Web Inspection
Free-Fall Sorting
Defect Detection
Object Classification
On-Device Analytics
SWIR Imaging
Stereo Vision
Semiconductor / PCB
What You Get

Concrete outputs. At every stage.

Every engagement closes with defined, signed-off deliverables. Here's what you take away.

Architecture & Feasibility Report
A go/no-go assessment with compute stack recommendation, latency projections, power budget, and a phased build plan — delivered before any development begins.
Implemented FPGA or NPU Solution
RTL, BSP, firmware, and SDK running on your target hardware — synthesised, integrated, and validated end to end. A system that works in your environment, not just on a bench.
Latency & Throughput Benchmarks
Formal performance report comparing pre- and post-implementation figures with CPU and GPU baselines — signed off against the targets agreed at project start.
Full Source Ownership
Complete RTL, MLIR-AIE kernels, driver stack, and application source — with documentation. No black-box IP in the critical path. When something needs changing, you can change it.
Integration Support & Handover
Team handover training, OEM qualification documentation, and integration guidance — so your engineering team can maintain, extend, and build on the system independently after delivery.
Get Started

Tell us your stack.

Tell us your camera interface, model, latency target, and OEM platform — we'll map the full FPGA + NPU architecture.

Contact Sales →
Last updated: March 2026