What does XNNG mean in UNCLASSIFIED

Xnng is a high-performance neural network inference framework developed by XNOR.ai. It is designed to enable efficient and fast deployment of neural network models on various hardware platforms, including mobile devices, embedded systems, and cloud servers.

Xnng offers a range of features, including:

• High performance: Optimized for low latency and high throughput inference.
• Cross-platform compatibility: Supports deployment on various hardware architectures, including ARM, x86, and RISC-V.
• Model optimization: Provides tools for model quantization, pruning, and compression to reduce model size and improve performance.
• Low memory footprint: Designed to minimize memory usage during inference.
• Open-source: Available under the Apache 2.0 license, allowing for customization and integration into existing projects.

Xnng supports a wide range of neural network models, including:

• Convolutional neural networks (CNNs)
• Recurrent neural networks (RNNs)
• Transformers
• Generative adversarial networks (GANs)
• And more

Xnng provides a straightforward deployment process:

1. Convert your model to Xnng format using the provided tools.
2. Integrate the Xnng library into your application.
3. Load the converted model into the Xnng engine.
4. Execute inference on input data to obtain predictions.

Benefits of using Xnng include:

• Improved performance and efficiency for neural network inference.
• Reduced latency and increased throughput for real-time applications.
• Support for a variety of hardware platforms, enabling deployment on diverse devices.
• Open-source availability, allowing for customization and integration into existing projects.