A drop-in compute module with known latency, power and cost for on-device inference.
A system-on-module and carrier board designed so product teams can add on-device inference without rebuilding their compute platform. The module ships with characterised numbers: latency, sustained throughput, peak memory and thermal behaviour under load.
Every customer arrived with a model already chosen and a power budget already promised. The two rarely matched, and the mismatch was usually discovered during integration rather than during architecture.
We inverted the order. The workload gets benchmarked on candidate silicon first, and only then is the module specified. Quantization runs against a fixed evaluation set with accuracy gates at each conversion step, so a regression is visible the moment it appears.
A SoM with an NPU sized to the measured workload, on a carrier board that exposes camera, audio, Ethernet and PCIe.
A conversion and deployment pipeline with an operator coverage map, so unsupported layers surface during benchmarking instead of integration.
A Linux BSP with the vendor NPU runtime, an inference service behind a stable local API, and a model update channel independent of firmware.
Hardware
AI
Embedded
Cloud