nn-weight-extractor: A Neural Network Weight Extraction Tool for Embedded System Deployment

nn-weight-extractor is an open-source tool focused on extracting weights from Keras and TensorFlow models and converting them into optimized binary formats. It is particularly suitable for model deployment on FPGAs, ASICs, and embedded systems, and supports optimization techniques like batch normalization folding to simplify the hardware acceleration process of deep learning models.

Deep learning models are often trained using frameworks like PyTorch and TensorFlow on GPU servers. However, when deploying to embedded devices, FPGAs, or ASICs, challenges such as large model files, limited resources, and strict inference latency requirements arise. nn-weight-extractor was created to address these challenges.

The core features of nn-weight-extractor include: 1. Weight extraction and batch normalization folding (merge batch normalization layers into the previous layer to reduce computational overhead); 2. Multi-framework compatibility (natively supports Keras and TensorFlow models); 3. Optimized export for hardware platforms (optimized output format for FPGAs, ASICs, etc.); 4. User-friendly operation interface (complete weight extraction and conversion in simple steps).

Supported model types include Convolutional Neural Networks (CNNs), Fully Connected Networks (FCs), and complex models with batch normalization layers. The output binary weight files have the following characteristics: compact storage (only retains inference parameters), hardware-friendly (optimized layout for embedded/accelerator devices), and cross-platform (portable standard format).

nn-weight-extractor is suitable for: 1. Edge computing devices (compress models to adapt to resource-constrained IoT devices); 2. FPGA acceleration (optimized weight format directly used in FPGA development toolchains); 3. ASIC chip design (used as input for ASIC flow validation and optimization); 4. Custom inference engines (provides a standardized weight input format).

The typical workflow for using nn-weight-extractor: 1. Prepare Keras/TensorFlow models; 2. Configure parameters according to the target hardware; 3. Perform extraction and apply optimizations (e.g., batch normalization folding); 4. Save the binary weight file; 5. Integrate into the target application or hardware platform. The entire process is completed within a few minutes, shortening the deployment cycle.

nn-weight-extractor is an open-source project, and community contributions are welcome: report issues/bugs, suggest new features, submit code improvements, and share use cases. It uses the GitHub collaboration process (Fork, Clone, Modify, Pull Request).

nn-weight-extractor fills the key gap between training and deployment. Through efficient weight extraction and conversion functions, it helps developers deploy AI models to platforms like edge devices, FPGAs, and ASICs, simplifying the process and improving performance. It is a practical tool for developers in the fields of embedded AI and hardware acceleration.