Frameworks shouldn't dictate your hardware. ONNX is the 'Universal Language' of machine learning, allowing any model to run on any edge device.
1The Universal Exchange Format
ONNX (Open Neural Network Exchange) is an open standard for representing machine learning models. It defines a common set of operators and a standard file format. This is transformative for Edge AI because it decouples Training (where PyTorch might be preferred) from Inference (where specialized hardware might only support certain runtimes). By exporting to .onnx, your model becomes 'Portable' across the entire tech stack, from cloud servers to mobile phones and IoT gateways.
Export: torch.onnx.export(model, dummy_input, 'model.onnx')
Status: UNIVERSAL_EXPORT_ACTIVE2Accelerating Everywhere
The power of ONNX Runtime (ORT) lies in its Execution Providers (EPs). Instead of writing separate code for every mobile chip, ORT uses EPs to automatically bridge the gap between the model and the hardware. Whether it's the CoreML EP on an iPhone, the NNAPI EP on Android, or the DirectML EP on a PC, ORT optimizes the execution for the specific device. For the most constrained environments, ORT Mobile allows you to build a custom runtime containing only the specific math needed for your model, reducing overhead to a minimum.
session = ort.InferenceSession('model.onnx', providers=['CPUExecutionProvider'])
results = session.run(None, {'input': data})
Status: PROVIDER_ACTIVE