Optimize image processing on Android using Halide
Introduction
Install and configure Halide for Arm development
Build a simple camera image processing workflow
Apply operator fusion in Halide for real-time image processing
Generate optimized Halide pipelines for Android using ahead-of-time cross-compilation
Integrate Halide into an Android project with Kotlin
Next Steps

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About this Learning Path

Skill level:  Introductory
Reading time:  3 hrs
Last updated:  05 Aug 2026
Skill level:
  Introductory
Reading time:
  3 hrs
Last updated:
  05 Aug 2026
Authors:Éliás Bálint, Arm
Dawid Borycki
Steve Suzuki, Arm
Arm IP:Cortex-A Cortex-X
Tags: Performance and Architecture Android Android Studio Halide CPP Kotlin Android Studio CMake
Authors:
  • Éliás Bálint, Arm
  • Dawid Borycki
  • Steve Suzuki, Arm
Arm IP:
Cortex-A Cortex-X
Tags:
Performance and Architecture Android Android Studio Halide CPP Kotlin Android Studio CMake

Who is this for?

This is an introductory topic for developers interested in learning how to use Halide for image processing.

What will you learn?

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

  • Learn the basics of Halide and set up your development environment
  • Build a simple real-time image processing pipeline with Halide
  • Make your image processing faster by combining operations in Halide
  • Use Halide pipelines in Android apps written with Kotlin

Prerequisites

Before starting, you will need the following:

  • Basic C++ knowledge
  • Android Studio with Android Emulator

Summary

AI-assisted

This summary was drafted with an approved AI-assisted workflow and reviewed by Arm contributors before publication. Human technical review remains part of the process so the final page reflects engineering rigor, accuracy, and Arm editorial standards.

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You’ll build and integrate a Halide-based image processing pipeline on Android. You’ll prototype Gaussian blur and thresholding, then use parallelization, tiling, and loop inspection to tune execution. After comparing fused stages with materialized intermediates, you’ll generate a cross-compiled library for an Android ABI. You’ll then integrate the library into a Kotlin app, and validate the processed frames on Android.

Frequently asked questions

AI-assisted

These FAQs were drafted with an approved AI-assisted workflow and reviewed by Arm contributors before publication. Human technical review remains part of the process so the final page reflects engineering rigor, accuracy, and Arm editorial standards.

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Which scheduling options should I try first to improve throughput?
Start with parallelization and tiling. Use print_loop_nest() to see how your schedule arranges loops and to verify changes behave as expected.
How do I choose between operator fusion and materializing intermediates?
Fuse stages to reduce memory traffic when results are used once and fit well in cache. Materialize with compute_root() or compute_at() for large filters or when an intermediate is reused by multiple consumers.
How do I confirm the cross-compiled pipeline targets the correct Android ABI?
Verify that the build uses the intended ABI, such as arm64-v8a, and ensure your Android project uses the same ABI. If the ABIs don’t match, your app might not load the pipeline library.
Do I need OpenCV in the Android app to follow the Learning Path?
No. OpenCV is used earlier to capture webcam frames for prototyping the pipeline on the host. You then call the compiled Halide pipeline from the Android app.
What should I check if the app fails to load the Halide pipeline library?
Check that the library was built for the same ABI as the app and that it’s included in the Android project configuration. Also confirm the pipeline artifact from the AOT step is packaged with the app.
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