OAID/Tengine

Health v2 · maintenance only

Adoption not part of the score

4531 stars · 981 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

Tengine is a lightweight, high-performance, modular deep learning inference engine developed by OPEN AI LAB for embedded and edge devices. It features a decoupled frontend/backend architecture supporting CPU, GPU, and NPU heterogeneous compute, with model conversion and quantization tools for frameworks like TensorFlow, PyTorch, and ONNX.

Use cases

• deploy cnn models on embedded arm devices
• run inference on npu hardware like verisilicon tim-vx
• convert onnx or tensorflow models to a lightweight runtime format
• quantize models to int8 for faster edge inference
• benchmark neural network speed on embedded boards
• deploy ai models in edge computing containers with superedge
• run deep learning inference on riscv or mips devices

When to choose

• you need efficient on-device inference on resource-constrained embedded hardware
• you want to target heterogeneous backends including cpu, gpu, and npu from one framework
• you need a small-footprint c/c++ runtime for aiot applications
• you require model conversion and quantization tooling for edge deployment

When to avoid

• you need training or fine-tuning capabilities rather than inference
• you primarily target cloud or server environments with abundant resources
• you depend on a broad ecosystem of prebuilt ops and community extensions like onnxruntime or tflite
• you need first-class support for large language models or transformer architectures

Facets

library · maturity active

machine-learning deep-learning llm-inference gpu-computing embedded benchmarking cli machine-learning deep-learning embedded-systems iot artificial-intelligence computer-vision cross-platform embedded cpp cross-platform inference-engine npu aiot edge-ai onnx model-conversion quantization tim-vx riscv tensorrt linux arm gpu android

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