LP ONNX on Android #2689
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LP ONNX on Android #2689
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GemmaParis
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Dec 22, 2025
GemmaParis
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GemmaParis
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The first chapter the "theory" chapter, perhaps it is too long, but I like your style of writing and the content conveyed. Let's see what the LP team thinks of this. The rest looks great! I have picked the need to upgrade from "Arm compute library" to "Arm Kleidi Kernels". See comments
| 1. Cross-platform support. ORT runs on Windows, Linux, macOS, and mobile operating systems like Android and iOS. It has first-class support for both x86 and Arm64 architectures, making it ideal for deployment on devices ranging from cloud servers to Raspberry Pi boards and smartphones. | ||
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| 2. Hardware acceleration. ORT integrates with a wide range of execution providers (EPs) that tap into hardware capabilities: | ||
| * Arm NEON / Arm Compute Library for efficient CPU execution on Arm64. |
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I would say "Arm Kleidi kernels accelerated with Arm Neon, SVE2 and SME2, for efficient CPU execution on Arm64"
| A typical ONNX workflow looks like this: | ||
| 1. Train the model. You first use your preferred framework (e.g., PyTorch, TensorFlow, or scikit-learn) to design and train a model. At this stage, you benefit from the flexibility and ecosystem of the framework of your choice. | ||
| 2. Export to ONNX. Once trained, the model is exported into the ONNX format using built-in converters (such as torch.onnx.export for PyTorch). This produces a portable .onnx file describing the network architecture, weights, and metadata. | ||
| 3. Run inference with ONNX Runtime. The ONNX model can now be executed on different devices using ONNX Runtime. On Arm64 hardware, ONNX Runtime takes advantage of the Arm Compute Library and NEON instructions, while on Android devices it can leverage NNAPI for mobile accelerators. |
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"Arm Kleidi kernels accelerated with NEON, SVE2 and SME2 instructions"
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