XiaoMi/mace
Mace
Mace is a mobile deep learning inference framework and hardware acceleration engine. It functions as a runtime for executing neural network models on mobile devices, distributing computations across CPUs, GPUs, and NPUs.
The project includes a cross-platform model converter for transforming pre-trained neural networks from various industry formats into mobile-optimized representations. It also provides a neural network obfuscator that converts model weights into source code to protect intellectual property from reverse engineering.
The framework manages on-device resources by optimizing memory allocation and adjusting chip power settings. It further addresses execution performance through mathematical algorithm optimization and computation splitting to maintain interface responsiveness.
Features
- On-Device Inference - Implements local execution of pre-trained deep learning models directly on mobile client hardware.
- Deep Learning Inference Engines - Provides a high-performance system for executing predictions of deep learning models on mobile hardware.
- Inference Acceleration Engines - Distributes deep learning computations across mobile CPUs, GPUs, and NPUs for maximum performance.
- On-Device Model Runtimes - Provides an execution environment to run language and deep learning models entirely on local mobile hardware.
- Inference Speed Optimizers - Increases operation speed by applying hardware acceleration and optimized mathematical algorithms to complex calculations.
- Deep Learning Acceleration - Provides hardware acceleration specifically for deep learning tensor operations and matrix multiplications on mobile chips.
- Hardware Acceleration - Offloads intensive deep learning computations to specialized mobile hardware like GPUs and NPUs.
- Cross-Format Model Importers - Provides tools to import pre-trained models from various industry-standard formats into a mobile-optimized runtime.
- Memory Footprint Optimizers - Minimizes memory consumption by optimizing data buffer allocation and reuse throughout the processing graph.
- Mobile Model Format Converters - Transforms neural network checkpoints from various industry formats into optimized representations for mobile interpreters.
- Resource Optimization - Manages chip power settings and memory allocation to balance energy consumption and performance.
- On-Device Resource Optimizers - Manages chip power settings and memory allocation to balance energy consumption and performance.
- Hardware Power Controllers - Adjusts chip power settings and scheduling hints to balance energy consumption with processing speed.
- Model Obfuscation Tools - Secures neural network models by converting weights into obfuscated source code to prevent reverse engineering.
Star history
How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.
