Learn how to utilize AI to master modern Android OS architecture and design patterns on embedded platforms.
Gain an overview of the Android operating system regarding its architecture, with a focus on internal architecture, extensibility, and adaptation to various embedded platform targets. Instead of only focusing on application programming using the Android SDK, you will cover “under the hood” concepts of Android, such as build and debug concepts, binding and memory sharing, the Media Codec API, as well as the Android Hardware Abstraction Layer (HAL). The course focuses on the application of AI tools to understand complex module relationships and design patterns within Android architecture. You will learn to successfully use AI to trace Binder communication flows and rapidly analyze logcat outputs and system traces for quick problem identification and resolution. Upon completing the course, you will be able to program and adapt Android quickly, powered by AI, to target use cases on specific embedded processors, mostly for applications in consumer electronics and media processing pipelines.
Course topics:
- Introduction, including brief Android history, differences between open source and AOSP projects, and an overview of Android architecture and all layers. Utilizing AI to navigate and comprehend massive AOSP directory structures and legacy codebases.
- Build subsystem, build templates, and device build configuration for specific embedded targets.
- Debug subsystem with adb, native debugging, ANR, addr2line, DDMS, traceview, and GDB utilization. Supporting deep debugging activities by safely feeding complex logcat outputs and system traces into AI for rapid anomaly detection and runtime behavior analysis.
- Android system-level concepts: init process, properties, binder, ashmem, JNI. Helping participants understand complex Binder IPC communication flow, service lifecycles, and cross-process system interactions using AI-guided code analysis.
- Multimedia stack in Android with the Media Codec API.
- Android HAL, extension, and manipulation. Assisting in understanding existing hardware abstraction framework modules to confidently adapt Android to specific hardware use cases.
- AI-Assisted project implementation for Android
Requirements
Software: RealVNC, Virtual Box, Chrome browser.
Hardware: Computer with an Internet connection, working speakers, and microphone.
Prior knowledge: Participants should have basic knowledge of programming in either Java or C/C++.
