Deploy AI to Any Edge Device
The YBPU compiler transforms AI models into hardware-optimized binaries for any edge target. From Raspberry Pi to Jetson to Rockchip—ship a C++ library in minutes.
Launch CompilerThree steps to edge deployment
No complex cross-compilation toolchains. Start directly in your browser.
Upload your AI model
Upload your trained deep-learning model files. Supports PyTorch, ONNX, and other major frameworks.
Select target hardware & precision
Choose your edge board and specify fp32 or fp16 precision. Optimization presets are also available.
Download package & deploy
Download the compiled libybpu_model.a library and headers. Start inference in just 3 lines of C++.
Every major edge platform, covered
ARM, x86, and RISC-V architectures supported, with new targets added continuously.
Raspberry Pi 3
ARMv8 / Cortex-A53
Raspberry Pi 4
ARMv8 / Cortex-A72
Raspberry Pi 5
ARMv8.2 / Cortex-A76
Rockchip 3328
RK3328 / Cortex-A53
Rockchip 3399
RK3399 / Cortex-A72+A53
Nvidia Jetson
Xavier / Orin / Nano
Qualcomm Snapdragon
Hexagon DSP + ARM
Arduino
AVR / ARM Cortex-M
RISC-V
RV64GC / SiFive
Intel CPU
x86_64 / AVX2
AMD CPU
x86_64 / Zen
Supported formats & outputs
Model Input
- 🔥PyTorch
.pt / .pth model files
- 📦ONNX
Open Neural Network Exchange format
- 🧠TensorFlow / TFLite
SavedModel and TFLite formats
Compiled Output
- 📚libybpu_model.a
Static library + C++ headers
- 🎯fp32 Package
Full 32-bit floating point precision
- ⚡fp16 Package
Half precision, faster inference
#include "ybpu_model.h"
YBPUModel model("model.param", "model.bin");
auto result = model.run(input_data);Ready to optimize?
Launch the compiler now or reach out for custom deployment support.