Fullmoonfever
Top 20
Another update a few hours ago to close the gap between Python model "training" and production C++ "deployment".
github.com
Brainchip-Inc/AkidaCPPInferenceDeploymentPackage
GitHub - Brainchip-Inc/AkidaCPPInferenceDeploymentPackage
Contribute to Brainchip-Inc/AkidaCPPInferenceDeploymentPackage development by creating an account on GitHub.
Brainchip-Inc/AkidaCPPInferenceDeploymentPackage
Akida C++ Inference Deployment Package
Overview
This package provides a complete, working example of deploying a trained Akida neural network model as a standalone C++ application on a Linux host. It bridges the gap between Python-based model training and production C++ deployment without requiring Python at runtime.What's Included
- Complete C++ inference pipeline (src/inference.cpp) with detailed comments explaining each API call
- Linux system implementation (src/system_linux.cpp) for required runtime hooks
- Model conversion script (scripts/convert_model.py) to transform .fbz models to C++ binaries
- Test input generator (scripts/generate_test_input.py) for validation against Python results
- Ready-to-build CMake configuration (src/CMakeLists.txt)
Target Use Case
You have:
A trained Keras model converted to Akida format (.fbz file)
Validated the model works in Python using the Akida SDK
An AKD1000 or AKD1500 device connected to a Linux host via PCIe
A standalone C++ application that runs inference without Python
Production-ready code you can integrate into your existing C++ codebase
Full control over the inference pipeline (batching, latency measurement, pre/post-processing)