Each category has different tradeoffs in compute density, power envelope, latency, and software ecosystem. Nuatara is designed to run across all of them — from the same codebase.
Each silicon type demands its own runtime, toolchain, and precision strategy. The Nuatara engine handles all of them through one integration.
Purpose-built for matrix operations — the dominant choice for vision and detection workloads on embedded SoCs. High TOPS/Watt. Each vendor ships its own SDK and model format.
Preferred for complex models demanding high throughput and flexible precision. Higher power envelope than NPUs; well-suited for robotics and autonomous systems with larger compute budgets.
Maximum flexibility and deterministic latency. Suited for latency-critical and custom pipeline use cases where cycle-accurate timing is a requirement. SDK fragmentation is significant.
ARM Cortex-A and x86 remain the baseline across edge gateways and industrial controllers. No dedicated inference acceleration — suitable for lighter models or where specialised silicon is unavailable.
Fixed-function AI chips deliver very high TOPS/Watt for operations they support. The tradeoff: model compatibility is narrow and SDK lock-in is complete. Best when the workload is stable and defined.
Your model runs on any of these targets without code changes. The engine selects the right runtime, handles precision per chip, and exposes a single inference interface regardless of silicon.
The Nuatara engine abstracts the entire deployment stack — so your team ships faster and your model runs everywhere.
The engine picks the right runtime for each target chip from your manifest. No manual wiring — add a new silicon target by updating config.
Precision is selected based on what each chip can actually do — FP32, FP16, or INT8 — with accuracy validation per deployment target.
One call signature across all hardware. Swap silicon in config — zero code changes in your application layer.
Vendor Yocto layers, NPU firmware, and kernel modules are managed by the engine — not your engineering team.
ONNX, LiteRT, TorchScript — bring your model in the format your training framework produces. No forced re-export.
No cloud dependency. Inference runs entirely on-device — deterministic, low-latency, privacy-preserving by default.
Talk to us about your target silicon, your model, and your deployment constraints.
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