aidlearning/AidLearning-FrameWork
AidLearning FrameWork
AidLearning-Framework is an integrated development platform for building and deploying AI applications on ARM-based devices. It combines Android and Linux operating systems running simultaneously on a single device, providing a unified runtime environment for cross-system AI development. The platform includes hardware acceleration across CPU, GPU, and NPU, with a unified API that automatically selects the optimal compute backend for inference.
The framework distinguishes itself by enabling Python-based AI projects to be packaged directly into Android APK files for installation on mobile devices, eliminating the need for manual environment setup. It provides a visual drag-and-drop interface for designing AI applications with pre-built components, allowing rapid prototyping without writing code from scratch. A remote desktop bridge streams the Linux desktop environment over WebSocket to browser or touchscreen clients, enabling remote development and testing from any device.
The platform supports on-device inference for deep learning and generative AI models, with automatic conversion between model formats and optimization for target hardware. It includes a real-time camera pipeline for AI-driven vision applications, and offers a curated repository of pre-trained AI models for selection and deployment. The dual-OS containerization runs Android and Linux in isolated containers sharing the same kernel, enabling seamless cross-system communication without external hardware.
Documentation covers the full development lifecycle from model selection and conversion through visual application design to APK packaging and deployment on ARM devices.
Features
- ARM Hardware Accelerators - Builds and deploys AI applications on ARM devices with hardware acceleration across CPU, GPU, and NPU.
- Python-to-APK Bundlers - Bundles Python-based AI projects into Android APK files for direct installation on mobile devices.
- Local On-Device AI - Provides a unified runtime for building and deploying AI applications locally on ARM devices.
- GPU-Accelerated Inference - Accelerates deep learning inference across CPU, GPU, and NPU with automatic framework conversion.
- ARM Multi-Hardware Accelerators - Automatically converts AI models and runs inference across CPU, GPU, and NPU on ARM-based mobile devices.
- On-Device Inference - Executes pre-trained models directly on local ARM hardware with hardware acceleration.
- Model Inference Accelerators - Provides a unified API that automatically selects the optimal compute backend for model inference.
- ARM AIOT Platforms - Combines Android and Linux environments with CPU, GPU, and NPU acceleration for building and deploying AI applications on ARM devices.
- Simultaneous Dual-OS Runtimes - Runs Android and Linux simultaneously on a single device for seamless cross-system AI development and deployment.
- Simultaneous Dual-OS Runtimes - Runs Android and Linux simultaneously on a single ARM device for seamless cross-system AI application development.
- Unified GPU Backend Abstractions - Abstracts CPU, GPU, and NPU access behind a single API that automatically selects the optimal compute backend.
- AI Application Deployment Platforms - Provides pre-built hardware and an AI toolchain for quickly deploying edge AI applications.
- AI Application APK Packagers - Packages Python-based AI projects into APK files for direct installation on Android devices.
- Python-to-Android Package Bundlers - Converts Python-based projects into Android APK files for direct deployment on Android devices.
- Dual-OS Container Runtimes - Runs Android and Linux in isolated containers sharing the same kernel for seamless cross-system communication.
- Simultaneous Dual-OS Runtimes - Runs Android and Linux simultaneously on one device to enable seamless cross-system interaction without external hardware.
- Simultaneous Dual-OS Runtimes - Runs Android and Linux at the same time on a single device for smooth cross-system communication without external interfaces.
- Dual-OS Runtime Managers - Manages the combined Android and Linux runtime environment on ARM-based devices for AI development.
- Generative Model Runtimes - Runs generative AI models on device to produce text or images from user prompts.
- Model Format Converters - Converts AI models between different formats and optimizes them for target hardware.
- Multi-Architecture Model Compilation - Converts models between frameworks and optimizes them for target hardware through a multi-stage pipeline.
- Visual AI Workflow Builders - Provides a drag-and-drop wizard interface for designing AI applications with pre-built components on Android devices.
- AI Application Designers - Ships a drag-and-drop wizard for rapid prototyping of AI applications with pre-built components.
- Camera Stream Integration - Captures and processes camera frames through a configurable chain of AI inference and image processing stages.
- Real-Time Processing Pipelines - Captures and processes camera frames through a configurable chain of AI inference stages.
- Remote Desktop Environments - Streams a full Linux desktop environment over WebSocket to browser or touchscreen clients.
- AI Pipeline Editors - Enables drag-and-drop assembly of AI pipelines by connecting pre-built functional blocks in a graphical editor.
