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Browse the latest content available to help you get started with your design, including articles, webinars, application developer guides, and sample code.
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If you are new to developing on Arm, read our getting started guides for application developer software and embedded software development:
Automatic Trash Classification with Raspberry Pi and Arm NN
Learn how to build an Arm NN-based application for an IoT device that performs automatic trash sorting through image analysis.
Dog Mood Detector with Google Cloud and Arm-based Raspberry Pi
Use Google AI Cloud to create a model for categorizing different dog sounds, then run the model on an Arm-based Raspberry Pi to listen for dog sounds and identify them.
Build end-to-end ML workflows with Arm
Use Arm Pelion Device Management, data management, and Mbed OS to build ML frameworks for IoT and endpoint applications.
Privacy-focused voice AI in intelligent robotics
Use a privacy-focused voice assistant, Snips, with MATRIX devices, an edge, and IoT development platform, and powered by Arm microcontrollers and Xilinx FPGAs, to deploy a voice-enabled robot with sensors, feedback loops, and motor control.
Run and profile Arm NN on the HiKey 960
Use Streamline to profile the performance of Linux applications that run TensorFlow models on Arm devices.
Configure the Arm NN SDK build environment for Caffe
Download, set up, build, and test Arm NN and its dependencies for Caffe.
Configure the Arm NN SDK build environment for ONNX
Download, set up, build, and test Arm NN and its dependencies for ONNX.
Configure the Arm NN SDK build environment for TensorFlow
Download, set up, build, and test Arm NN and its dependencies for TensorFlow.
Configure the Arm NN SDK build environment for TensorFlow Lite
Download, set up, build, and test Arm NN and its dependencies for TensorFlow Lite.
Deploy a Caffe MNIST model using the Arm NN SDK
Import a Caffe model into Arm NN, optimize and load it onto a compute device.
Improve your ML workflow using the Arm NN SDK
Walk through a typical ML workflow with a 32-bit floating-point Convolutional Neural Network.
Quantize neural networks to 8-bit using TensorFlow
Improve the performance of neural network models on Arm compute devices using TensorFlow.
Deploy a quantized TensorFlow Lite MobileNet V1 model
Develop a lightweight image classification mobile application using the Arm NN SDK.
Deploy a TensorFlow MNIST model using the Arm NN SDK
Import a TensorFlow model into Arm NN, optimize and load it onto a compute device.
Build Arm NN custom backend plugins
Write a custom backend for Arm NN, with an example custom backend to illustrate the process.
Implement a neural style transfer on Android with Arm NN APIs
Learn how to build a style transfer Android application with Arm NN APIs.
Implement classical machine learning with Arm CMSIS-DSP libraries
Learn how to use the new Support Vector Machine and Naive Bayes Classifier libraries for classification.
Arm NN accelerated object detection with Autoware
Use Autoware.AI and Arm NN to build and run a real-time object detection system.
Deep-learning AI on low-power microcontrollers: MNIST handwriting
Train a TensorFlow model with MNIST and convert your model to TensorFlow Lite. Create the embedded application to generate sample MNIST data for embedding and testing the MNIST images.
Teach your Raspberry Pi - Episode 1 Yeah, World
Train your Raspberry Pi to burst into applause when you raise your hands in celebration.
Teach your Raspberry Pi - Episode 2 Multi-gesture Recognition
Train your network to recognize gestures in many situations and learn how to use larger datasets.
Cross-compile Arm NN and TensorFlow for the Raspberry Pi
Cross-compile Arm NN on an x86_64 system to work around the limited memory of the Raspberry Pi.
Run AlexNet on Raspberry Pi with Arm Compute Library
Develop Convolutional Neural Networks using just the Compute Library and a Raspberry Pi.
Profile AlexNet on Raspberry Pi and HiKey 960
Use Streamline to profile the performance of an AlexNet sample application on two hardware platforms.
Create a text-to-speech engine with Google Tesseract and Arm NN on Raspberry Pi live
Learn how to choose and convert an existing TensorFlow model to work with Arm NN and explore best practices for model conversion and implementing Arm NN solutions.
Accelerating ML inference on Raspberry Pi with PyArmNN
Learn how to use Python APIs for Arm NN inference engine to classify images as Fire versus Non-Fire.
Join our series of AI Virtual Tech Talks on developing the latest AI technologies.
Build an Arm Cortex-M voice assistant with Google TensorFlow Lite
Perform machine learning inference on an Arm Cortex-M microcontroller with TensorFlow Lite for Microcontrollers.
Convert a neural network for Arm Cortex-M with CMSIS-NN
Convert a neural network from any framework into an implementation on an Arm Cortex-M device.
Perform image recognition on Arm Cortex-M with CMSIS-NN
Improve the performance and energy efficiency of real-time image recognition on an Arm Cortex-M7 processor.
Deploy a Caffe model on OpenMV using CMSIS-NN
Run a smile detection program on an Arm Cortex-M7 processor using a Caffe model.
TinyML Application Development for Everyone
Learn how to use your gestures to train classifier in TensorFlow and then deploy to an Arm Cortex-M-based board running Mbed OS.
Build your own Harry Potter wand with TensorFlow Lite Micro
This is an experiential workshop that focuses on the use of TensorFlow Lite on a low-power microcontroller to perform machine learning.
Optimizing neural networks for mobile and embedded devices with TensorFlow
Prepare TensorFlow models for deployment on Android, Linux, and iOS.
Deploying cloud-based ML for speech transcription
Set up client-server speech transcription deployed as a service running on cloud-hosted Arm servers.
Powering the edge: driving optimal performance withEthos-N77 Processor
Repurposing a CPU, GPU, or DSP is an easy way to add ML capabilities to an edge device. However, where responsiveness or power efficiency is critical, a dedicated Neural Processing Unit (NPU) may be the best solution. In this paper, we describe how the Arm Ethos-N77 NPU delivers optimal performance.
Machine learning on Arm Cortex-M Microcontrollers
ML algorithms are moving to the IoT edge due to latency, power consumption, cost, network bandwidth, reliability, privacy, and security considerations. Read how the open-source CMSIS-NN library can help you maximize the performance of neural network solutions that deploy ML algorithms on low-power Arm Cortex-M cores.
How to add intelligent vision to your next embedded product
Embedded vision enhances solutions in a broad range of markets including automotive, security, medical, and entertainment. In this paper, we explain how to add intelligent vision to your next embedded device.
How to migrate intelligence from the cloud to the device
Arm and its partners are enabling a step-change increase in on-device processing, from tiny microcontrollers to multicore gateways. In this paper, we explain how Arm and its partners are approaching this unique problem.
Deploying always-on face unlock: integrating face identification, anti-spoofing, and low-power wakeup
Accurate face verification is a challenge due to the number of variables that are involved. In this paper, we look at a new approach that combines classic and modern machine learning techniques. This approach achieves 98.36% accuracy, runs efficiently on Arm ML-optimized platforms, and addresses key security issues.
Packing neural networks into end-user client devices: how number representation shrinks the footprint
Work is ongoing to simplify neural network processing so that more algorithms can run on edge devices. One approach eliminates complexity by replacing floating-point representation with fixed-point representation. In this paper, we take a different approach and recommend a mix of the two representations, to reduce memory and power requirements while retaining accuracy.
The power of speech
Voice-activated assistants that use keyword spotting have become more widespread. In this paper, we borrow from an approach used for computer vision to create a compact keyword spotting algorithm. This algorithm supports voice-driven commands in edge devices that use a very small, low-power microcontroller.
The new voice of the embedded intelligent assistant
Intelligent assistance is becoming vital in our daily lives and the technology is taking a big leap forward. In this paper, Recognition Technologies and Arm provide technical insight into the architecture and design approach that is making the gateway a more powerful, efficient place for voice recognition.
Discover tips and techniques for your Arm-based machine learning projects with our library of webinars.
Enable Arm IP to be used in safety-related systems for markets such as automotive, industrial, avionics and others. The Safety Package includes a set of documentation artifacts or evidence and any specific safety claims and results of certification activities that Arm holds for an individual IP.
View technologies from Arm partners which can service the compute needs for the whole car, including advanced driver assistance and autonomous systems.
The Arm Functional Safety Partnership Program showcases a range of functional safety partners who specialize in the areas of software and tools, design services and training services.
Principles of high performance
Explains the cornerstones of high performance and how to achieve it, along with what to look out for.
Tile based rendering on Mali
An introductory guide to understanding the Mali Rendering Architecture.
Understanding render passes
How render passes apply to Mali’s tile-based GPU architecture on different APIs.
An in-depth look into macro-scale pipelining of workloads, the means by which we keep the GPU busy all of the time, and some of the common reasons for that frame level pipeline to stall.
Accelerating 2D applications
How to improve a device's battery life by reducing device energy use and preventing thermal throttling by using specific aspects of 3D rendering to accelerate performance in 2D applications.
The Valhall shader core
This guide describes the top-level layout and the benefits of and shader core functionality of a typical Mali Valhall GPU programmable core, the forth generation of Mali GPUs. The Valhall family includes Mali-G5x, and Mali-G7x from 2018 onwards.
The Utgard shader core
This guide describes the top-level layout and the benefits of and shader core functionality of a typical Mali Utgard GPU programmable core, the first generation of Mali GPUs. The Utgard family includes the Mali-400, Mali-450, and Mali-470 series of products.
The Midgard shader core
This guide discusses a typical Mali Midgard GPU programmable core. Midgard is the second-generation Mali GPU architecture, and the first to support OpenGL ES 3.0 and OpenCL. The Midgard family includes the Mali-T600, Mali-T700, and Mali-T800 series of products.
The Bifrost shader core
This guide describes the top-level layout and the benefits of and shader core functionality of a typical Mali Bifrost GPU programmable core, the third generation of Mali GPUs. The Bifrost family includes the Mali-G3x, Mali-G5x, and Mali-G7x series of products.
The benefits of buffer packing on memory bandwidth
This guide explains how to make best use of the limited memory bandwidth available to your application on your target device and what memory bandwidth areas can be made more efficient.
Adaptive Scalable Texture Compression
This guide provides information about how you can use Adaptive Scalable Texture Compression (ASTC) to optimize the performance of your apps.
Understanding numerical precision
This guide explores the different levels of numerical precision available for a GPU. It explains the advantages of using narrower data types, and when to consider using the higher precision types available instead.
Vulkan® is a powerful graphics and compute API that provides high-efficiency, cross-platform access to modern GPUs used in a wide variety of devices from PCs and consoles to mobile phones and embedded platforms.
OpenGL® ES is a royalty-free, cross-platform API for rendering advanced 2D and 3D graphics on embedded and mobile systems - including consoles, phones, appliances and vehicles.
OpenCL™ (Open Computing Language) is the open, royalty-free standard for cross-platform, parallel programming of diverse processors found in personal computers, servers, mobile devices and embedded platforms.
A collection of videos and presentations of VR Presentations from Arm and partners such as Unity, Epic Games and nDreams.
See a selection of VR demos running on Arm Mali architecture, these demos have been created by Arm and our partners.
VR guides and white papers
The latest white papers from the Arm Graphics and Multimedia teams. They look at potential future solutions for graphical development on mobile and use cases like virtual reality.
Find out more on the Arm Community with our virtual reality blogs.
View all the latest Arm Demos in one place. From Twitch live streams to our famous Ice Cave demo.
Frequently asked questions
The question we get asked the most. Feel like there is something missing? Let us know!
Arm Mali OpenGL ES and Vulkan sample codes will teach you techniques that can be adapted for use in your own applications. They provide instructions for creating a sample and allow you to see how they were created at a code-level.
See below the complete selection of tutorials including sample code and presentations from different events. These contain a wide selection for different skill levels and include - Getting started tutorials, advanced techniques and optimization.
White Papers on future applications that have been produced for developers and Arm Partners. Such topics include 360-degree video rendering in VR and foveated rendering.
Geometry best practices
This guide highlights geometry optimisations for 3D assets, that can make a game more efficient and run better on mobile.
Textures best practice
This guide summarizes texture optimizations that will help your games to run smoother and look better on mobile.
Material and Shader best practice
This guide summarizes a number of material, shader and transparency optimizations that will help games run smoother.
Arm guide for Unity developers
We have collated all the hints, tips, and techniques which have arisen during projects that our demo team have worked on, which we hope will benefit you as a beginner or intermediate level developer.
Optimizing mobile gaming graphics with Unreal Engine 4
This guide is designed to help you create applications and content that make the best use of Unreal Engine 4 on mobile platforms, especially those with Arm Mali GPUs.
Mali best practices
The best practices guide for developers optimizing for Mali GPUs and recommendations for efficient API usage.
Mali Bifrost and Valhall OpenCL programming guide
Our OpenCL guides provide advice and information to developers on how to improve performance on platforms performing complex algorithms.
Mali Midgard OpenCL programing guide
Our OpenCL guides provide advice and information to developers on how to improve performance on platforms performing complex algorithms.
OpenGL ES 3.X developer programming guide
Learn the key OpenGL ES 3.x API features and extensions, as well as best practises on how to optimize your OpenGL ES 3.x application for the Arm Mali architecture with our programming guide.
Arm guide to RenderScript best practice
This guide provides advice and information to developers working with RenderScript on Arm Mali GPUs.
Arm Mobile studio
Arm Mobile Studio is a software suite targeted at Android developers, allowing the easy detection of bottlenecks on any Android device by enabling the visualization of all performance data in the system.
Mali Texture Compression Tool compresses textures using ETC and ASTC texture compression formats. It includes command line tools, and a GUI for comparing the original, and compressed textures.
OpenGL ES Emulator
Mali OpenGL ES Emulator is a library that maps OpenGL ES API calls to the desktop OpenGL API. Downloads are available on both Windows 64-bit and Linux 64-bit builds.
ASTC Evaluation Codec
The ASTC Evaluation Codec is a command-line executable that compresses and decompresses images using the Adaptive Scalable Texture Compression standard.
Optimization is the process of taking an application and making it more efficient. Browse our collection of resources for optimising for an Arm Mali GPU, including tools and best practice guides.
Are you a graphics developer targeting mobile platforms? Access a wealth of resources in our graphics solutions space. Find best practise guides, sample code, SDKs, and more to help build and optimise your project
Presentations from events such as Game Developers Conference and Unity's Unite event series.
Learn more about the tools for Unity:
Arm based demos made with Unity.
The work from some of Arm's research and demo creation has made its way onto the Unity Asset Store. We hope this will help developers use this research in their own games and applications by learning directly from the project files.
Arm guide for Unity developers
The Our guides for Unity developers show you how to get the most out of Unity when developing under the unique challenges of mobile platforms. See the topics:
Learn more on the Arm Community.
See the presentations about using Unreal Engine with Arm.
Arm tools for Unreal Engine
Profile, optimize and debug your UE4 game apps with Arm Mobile Studio. Arm works closely with Unreal Engine to simplify the use of our tools with their game engine. See topics:
See a selection of UE demos running on Arm Mali architecture, created by Arm and our partners.
Read our guide for UE4 developers on how best to improve rendering quality in mobile VR, including examples and explicit guidance on how to implement our suggestions. You can download the samples used for the examples to learn more about these techniques.
Read the guide
Download sample project files
Arm guide for Unreal Engine developers
This document is designed to help you create applications and content that make the best use of Unreal Engine 4 on mobile platforms, especially those with Arm Mali GPUs.
Learn more on the Arm Community with Unreal Engine related blogs.
Open Source and Commercial HPC applications that have been ported to Arm
Popular open source and commercial compilers for high performance computing applications.
Powerful debug tools that can help debug complex parallel HPC applications quickly and efficiently.
Leading open source and commercial profiling tools to quickly identify the performance bottlenecks in your HPC application.
Leading Linux/BSD distributions for HPC on Arm.
Tools and Libraries that allow parallelism in HPC applications on Arm.
Open source and commercial math libraries that you can use to make your HPC application go faster.
Open Source and Commercial File Systems for HPC on Arm.
Open source and commercial workload managers for HPC on Arm
Python packages suited for scientific computing and other HPC applications.
Armv8-A 64-bit platforms
The server and HPC communities exploit leading tools from Arm to provide fast and intuitive development, lightning speed debug, customizable parallel profiling, and the ability to pinpoint performance issues more efficiently than ever.
Scalable Vector Extension (SVE)
SVE is a vector extension for AArch64 execution mode for the A64 instruction set of the Armv8-A architecture.
Arm Forge is the leading server and HPC development tool suite in research, industry, and academia for C, C++, Fortran, and Python high performance code on Linux.
Arm Performance Reports
Arm Performance Reports is a low-overhead tool that produces one-page text and HTML reports summarizing and characterizing both scalar and MPI application performance.
A collaborative community effort to provide common, pre-built ingredients required to deploy and manage an HPC Linux cluster.
Arm HPC ecosystem partners delivering the best-in-class converged supercomputing building block products and services. These partners help you add your own unique value to the Arm architecture.
A collection of presentations delivered over the past 12 months by Arm staff and partners at international events, and during live webinars hosted by Arm.
Develop on Arm
Learn about how to get the maximum performance for your high-performance and scientific codes on Arm.
Port applications to Arm
Most applications will port to Arm with little or no modification. Follow these steps to port your application.
Build common apps on Arm
How to build many common scientific applications, benchmarks and libraries using Arm HPC tools:
Arm tools help
If you are using Arm Allinea Studio, or Arm Forge (Arm DDT, Arm MAP, Arm Performance Reports), see our topics and tutorials to help you get the most out of these tools.
Scalable Vector Extension (SVE) is the next-generation SIMD instruction set for Armv8-A (AArch64). Learn how this new extension is revolutionizing HPC.
Arm Architecture Reference Manual Supplement - The Scalable Vector Extension (SVE), for Armv8-A
See the full SVE instruction set specification, downloadable in a convenient .zip format containing the full PDF, XML and HTML components of the specification.
Scalable Vectorization for LLVM
This talk, presented at 2016 LLVM Developers’ Meeting, covers the changes made to LLVM IR to support vectorizing loops in a vector length agnostic manner, as well as improvements in vectorization enabled by the predication and gather and scatter features of the SVE.
Get started on Arm
Find out more about getting started on 64-bit Arm (AArch64).
See all the documentation for the Arm HPC toolset.
A collection of White Paper resources related to HPC on Arm.
From building and automation to testing and deployment, developers can explore a wide range of Continuous Integration and Continuous Deployment (CI / CD) solutions, all on Arm.
Containers and virtualization
Developers can explore a range of compute platforms, tools, and services for building and packaging applications, and managing containerized workloads.
Languages and libraries
Arm and partners participate in and contribute to a list of languages and libraries to ensure the growth and robustness of the Arm software ecosystem.
View Arm Blueprints with Akraino Edge and DPDK optimizations for Arm
A wide range of operating systems are available for developers who want to build on Arm.
High-performance tools for developing Arm-based server and HPC applications
Explore the Arm-based Universal Customer Premise Equipment, and the partner solutions
From multithreading for cost-effective performance to load balancing and distributed data-storage, developers can count on a wide range of workload solutions that are optimized to run on Arm.
Find your platform and develop on Arm for Arm
Download bare-metal hardware
Arm and Packet have partnered to make powerful Neoverse-based Armv8 bare-metal infrastructure. This infrastructure includes the latest generation Ampere and Marvell systems, available for open-source software developers to build, test, and optimize for Arm64 architecture.
View the open-source projects and initiatives to enable new hardware capabilities and optimize networking infrastructure performance.
View the leading companies within the Arm ecosystem delivering hardware, software, and professional services to help you realize your market goals.
Find out more about the infrastructure external organizations.
If you are using computer vision techniques like image classification and object detection, here are some resources from Arm and partners that can help.
If you are using audio processing techniques like voice and sound recognition, and keyword detection, here are some resources from Arm and partners that can help you build sound-aware IoT devices.
If you are using gesture recognition or anomaly detection techniques for use cases such as predictive maintenance, here are some resources from Arm and partners that can help.
Digital Signal Processing
Access resources from Arm and partners that will help you implement signal processing on Arm processors.
Learn about training ML models and running inference on your device.
Arm and partners offer tools and materials to support your needs as an IoT and embedded software developer.
Languages and libraries
Explore open source languages and libraries for your IoT development.
Explore operating systems that help you to build IoT applications efficiently.
Get started developing and managing IoT devices with Arm Pelion.
Debug and optimization
Improve code performance and reduce power consumption while debugging your code.
Explore resources for widely used connectivity protocols for IoT applications.
Understand how Platform Security Architecture APIs can make it quicker and easier to implement security.
For designing an SoC for an IoT application, Explore Arm processor IP, multimedia technology, system IP, and Artisan physical IP.
Classical ML with CMSIS-DSP
Learn how to efficiently run classical supervised learning techniques with CMSIS-DSP library on your Cortex-M device.
Convert a NN for Cortex-M
Learn how to convert a neural network from any framework into a model that can run efficiently on a Cortex-M device with CMSIS-NN.
Basics of TensorFlow Lite for Microcontrollers
Learn the basics of TensorFlow Lite for Microcontrollers, using the full end-to-end workflow: training a model, converting it for use with TensorFlow Lite, and running inference on a microcontroller.
An open approach to IoT
This whitepaper describes a simple path to developing secure Cortex-M based IoT devices with Arm and AWS. The paper shows how this collaboration provides choice and scalability for IoT developers.
The future of AI
This webinar provides a technical overview of the Cortex-M55 and Ethos-U55 processors, new Arm IP that will bring a 480x ML performance uplift to future endpoint AI applications.
Proof-of-concept: computer vision on Arm
This webinar shows how future Arm processors and the Arm NN software library will enhance computer vision for high-performing IoT.
If you’re just getting started developing IoT applications, Arm educational resources will help you understand the fundamentals.
64-bit Android development
Whether you're porting existing 32-bit code, or writing completely new software, you'll need to understand how to make sure your app is ready to support 64-bit devices.
Get started with developing on Android.
The Android NDK (Native Development Kit) is a toolset that lets you implement parts of your app in native code, using languages such as C and C++.
The Android Open Source Project (AOSP) repository offers the information and source code required to create custom variants of the Android OS.
The guides and API reference required for your projects.
Neon intrinsics: getting started on Android
In this article, you will see how to set up Android Studio for native C++ development, and to utilize Neon intrinsics for Arm-powered mobile devices.
How to Truncate Thresholding and Convolution of a 1D Signal
In this article, you will see an approach that can be easily employed to write efficient code that can be useful for signal and image processing, neural networks, or game applications.
Explore partner resources for infrastructure development based on Arm.
Learn the Architecture
Learn how the Arm architecture works with our series of guides. From the fundamentals to more advance concepts.
Arm Mobile Studio is a suite of free-to-use tools. These tools help game and app developers to reach more of the mobile market by efficiently optimizing and debugging high-end content for Android devices.
A collection of resources for application development on Arm Mali GPUs.
Arm NN is a neural network inference engine for CPUs, GPUs, and NPUs. It bridges the gap between existing NN frameworks and the underlying IP.
Arm Neon technology is an advanced Single InstructionMultiple Data (SIMD) architecture extension for the Arm Cortex-A and Cortex-R series processors. Including the Arm Neon intrinsic lookup.
Available on Arm-powered PCs, a collection of resources to learn more about the Windows OS and get started developing apps.
Explore partner resources for HPC development based on Arm.
Arm empowers your academic research with access to a range of commercial Arm IP, tools and resources. Find the program that best suits you and your project.
Strong collaborative links with the academic community are highly valued within Arm Research, and we work with many individuals, groups and institutions worldwide.
Research Enablement Kits are designed to help researchers get the most out of widely available Arm technologies, including DesignStart IP and the gem5 open source simulator.
Arm Research publishes their work on a wide variety of topics in highly regarded conferences and journals around the world. Find out more about our most recently published work: