Model selection, quantization, and optimization matched to your hardware's actual compute, memory, and power budget — not a best-effort port.
A model that runs fine on a workstation GPU often doesn't survive the trip to an edge device. We work backward from your real constraints — available RAM, compute budget, power envelope — and choose, quantize, and optimize a model that actually fits.
Because we also build the hardware and firmware underneath it, the model isn't optimized in isolation — it's tuned against the board it's shipping on.
Choosing or adapting an architecture that fits your task and your hardware's real limits.
Precision reduction (INT8/INT16) tuned to preserve accuracy while cutting compute and memory load.
Pruning and graph optimization matched to your target NPU, MCU, or FPGA accelerator.
Runtime integration so inference runs reliably inside your embedded system, not a demo shell.
Real measurement of inference time and power draw on your actual hardware, not a datasheet estimate.
Pairing model deployment with custom FPGA or NPU acceleration where the workload demands it.
| Frameworks | TensorFlow Lite, ONNX Runtime, PyTorch Mobile |
| Target hardware | MCUs, NPUs, and FPGA-based accelerators |
| Optimization | Quantization, pruning, graph-level optimization |
| Deliverables | Deployed model, runtime integration, performance report |
Real-time vision or sensor inference, offline-capable devices, and products where latency or connectivity rules out cloud inference entirely.
Tell us the model and the target device — we'll tell you what's actually achievable.
Start an edge AI project →