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Android On-Device AI and ML on Arm Cortex CPUs
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TOPIC 1

Build AI/ML Android Apps

Get Started running your ML and AI workloads on Arm Cortex CPUs.

 

Profile the Performance of AI and ML Mobile Applications on Arm

  • This is an introductory topic for software developers who want to learn how to profile the performance of machine learning (ML) models running on Arm devices.

ExecuTorch for On-Device AI

Get Started with AI and ML on Unity with Unity Sentis and ML Agents

  • A series of videos, learning paths, blogs and tutorials on how to bring AI and ML into your Unity project on Arm-based mobile devices.

Google’s AI Edge’s Mediapipe Acceleration on Arm-Based Android

Get Started with OpenCV on Android

TOPIC 2

Build GenAI Android Apps

Learn how to quantize neural network (NN) models and run large language models (LLMs) on mobile.

Create and train a PyTorch model for digit classification using the MNIST dataset

  • This is an advanced guide for developers on creating PyTorch feedforward neural networks for digit classification and optimizing them for Android applications.

Build an Android Chat App with Llama and ExecuTorch

  • A get-started guide to learn how to set up ExecuTorch and quantize models without sacrificing significantly the model accuracy.

GenAI on Arm Cortex CPUs

  • A blog covering a virtual assistant demo, which first used Meta’s Llama2-7B on mobile via a chat-based app, and has since expanded to include the Llama3 model and Phi-3 3.8B.

NN Model Quantization

  • A blog covering the use of quantization for neural network (NN) models, which is critical for deploying GenAI models on mobile devices and edge platforms.
TOPIC 3

Accelerate GenAI, AI, and ML

Accelerate your AI/ML framework, tools, and cloud services with open-source Arm libraries and optimized Arm SIMD code.

 

Arm Kleidi

  • Arm Kleidi open-source libraries are a lighter weight performance library than ACL for accelerating AI and ML workloads and frameworks.

Arm KleidiAI

Arm KleidiCV

  • The KleidiCV library is designed for image processing and integrates into any CV framework to enable optimum performance for CV workloads on Arm.

Arm SIMD-Extensions

  • Optimize your AI/ML workloads with Arm SIMD code, either in assembly or using Arm Intrinsics in C/C++, to leverage huge performance gains.

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Ben Clark

Ben Clark is an Arm Staff Software Engineer and developer advocate, researching and publishing the best use of Arm technologies in consumer devices. He has a graphics background and a keen interest in machine learning.

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