MegCC is a deep learning model compiler designed for extreme portability and efficiency, targeting embedded systems and mobile devices. It enables developers to deploy neural networks with minimal runtime overhead, achieving small binary sizes and fast inference.

How It Works

MegCC leverages the MLIR infrastructure to compile models, generating highly optimized, hand-tuned computation kernels. It supports static and dynamic tensor shapes and can bundle essential computer vision operators, eliminating the need for large external libraries. The compilation process includes static memory planning and model optimizations, resulting in low memory usage and instant boot times during inference.

Quick Start & Requirements

• Install: Download release compiler suit from the release page.
• Prerequisites: Linux, Android, TEE, BareMetal. Supports Arm64/ArmV7/X86 backends.
• Documentation: Compiler doc, How-to-use, Chinese doc.

Highlighted Details

• Extremely lightweight runtime: 81KB for MobileNet v1 (fp32).
• High performance through expert-optimized kernels.
• Portable code generation for various environments, including bare-metal.
• Low memory usage and instant inference startup via compile-time optimizations.

Maintenance & Community

• No specific community links (Discord/Slack) or notable contributors/sponsorships are mentioned in the README.

Licensing & Compatibility

• License: Apache License, Version 2.0.
• Compatibility: Permissive license suitable for commercial and closed-source use.

Limitations & Caveats

The README does not explicitly detail supported operators or provide benchmark comparisons against other compilers. Community engagement channels are not readily apparent.