Accelerate multimodal Voice Assistant performance with KleidiAI and SME2

Who is this for?

This is an introductory topic for developers who want to implement a multimodal pipeline for a Voice Assistant application and accelerate the performance on Android devices using KleidiAI and SME2.

What will you learn?

Upon completion of this Learning Path, you will be able to:

• Learn about the multimodal Voice Assistant pipeline and different components used.
• Learn about the functionality of ML components used and how these can be built and benchmarked on various platforms.
• Compile and run a multimodal Voice Assistant example based on Android OS.
• Optimize performance of multimodal Voice Assistant using KleidiAI and SME2.

Prerequisites

Before starting, you will need the following:

• An Android phone that supports the i8mm Arm architecture feature (8-bit integer matrix multiplication).
• An Android phone with support for SME (Scalable Matrix Extension) instructions, required for SME performance checking
• This Learning Path was tested on a Vivo X300 Pro.
• A development machine with Android Studio installed.
• Arm Performance Studio installed. Follow the Arm Performance Studio install guide for instructions.