Arm
Public University • UK
Showing 11 courses from Arm
Advanced Armv8-M Features
Arm (via Coursera)
The final course covers more advanced and optional features that might be configured in a Cortex-M system. These features could help with particular project requirements such as security and performance. Not all of these topics might be relevant for any given Cortex-M project, so feel free to pick and choose which topics, if any, apply to you.
Computer Architecture Essentials on Arm
Arm (via Coursera)
Whether you’re downloading an app, streaming a video, or clicking a mouse, modern microprocessors are the engines powering your digital life. Arm’s 30-year-plus history of designing energy and power-efficient microprocessors helped fuel the smartphone revolution, and today from the tiniest sensors to the world's most powerful supercomputers, Arm is building the future of computing. This course will provide you with a deep understanding of the architecture of modern microprocessors. We'll start your learning journey by covering the basics of computer architecture, such as definitions and a description of key components of a microprocessor. We'll then take a deep dive into important computer architectural concepts and processes. For example, you'll learn how pipelining, branch handling and cache memories can improve the performance of a single-cycle microprocessor. We'll then introduce you to the world of superscalar processors, and how to maximise performance using out-of-order instructions. Our lab exercises will bring to life the theory discussed in the course through a range of simulation tools. Along the way, we'll provide you with insights from key engineers at Arm, illustrating how and why certain design choices were made so you’ll have the real-world context behind these pivotal architectural decisions. With over 250 billion Arm-based chips deployed by our vast ecosystem of partners, Arm technology is present wherever computing happens. Be a part of this thriving community and enrol in Computer Architecture Essentials on Arm today. This course is aimed at learners who have a basic understanding of computer organisation and programming languages. If you are completely new to the world of microprocessors, we suggest you take our Introduction to Microprocessors course on Coursera.
Arm Cortex-M Processors Overview
Arm (via Coursera)
This course is designed for anyone wishing to learn about the range of Cortex-M processors and the different resources that can help you with your Cortex-M project. The course begins with a bit of history about Arm processors and the Arm architecture, covering the differences between the M-profile and other architecture profiles like A-profile and R-profile. This course is suitable for beginners or people without an engineering or computer science background. The introductory material also sets the scene for the courses 2, 3 and 4.
Introduction to AI
Arm (via Coursera)
Discover the fundamental concepts behind artificial intelligence (AI) and machine learning in this introductory course. Explore the various types of AI, examine ethical considerations, and delve into the key machine learning models that power modern AI systems. Whether your goal is to work directly with AI, strengthen your software development skills, or enhance your data science expertise, this course provides an essential foundation for success in the field.
Introduction to Microprocessors
Arm (via Coursera)
Introduction to Microprocessors is targeted at anyone with an interest in learning the basics of computer architecture, microprocessors and CPUs. Do you want to understand how the central processing unit (CPU) of a microprocessor works? How the code you type is actually executed by your computer? Presented by engineers from Arm, this course provides you with an introduction to the central components of processors including: the Arithmetic Logic Unit, or ALU, and the arithmetic and logical operations it carries out; the Fetch-Decode-Execute cycle or FDE within different architectures; pipelining, or how a CPU decides what to prioritize and the challenges faced when doing so; types of memory and their uses; the process that high level code, such as C, goes through to get converted into machine code; assembly code, an interim step between high level and machine level code; and how assembling and compiling work together to produce object code or executable files. To get the most out of this course, learners should already be familiar with basic Boolean algebra and have experience of programming in object code, such as Python or C.
Optimizing Generative AI on Arm Processors
Arm (via Coursera)
AI models are becoming increasingly powerful—but also increasingly demanding. As Generative AI moves from cloud data centers to mobile phones, autonomous systems and embedded IoT devices, the need to optimize performance across diverse hardware environments has never been more critical. Arm-based processors power more than 300 billion devices globally, from smartphones to hyperscale cloud servers, making them a key foundation for efficient AI deployment across the compute landscape. To meet this growing demand, learners need the skills to translate machine learning models into real-time, hardware-aware implementations across Arm-based platforms. Optimizing Generative AI on Arm Processors: from Edge to Cloud is designed for intermediate machine learning practitioners who want to bridge the gap between model design and deployment efficiency. Rather than revisiting ML fundamentals, this course dives straight into performance engineering for Generative AI on Arm-based platforms, including mobile, edge and cloud environments. You’ll explore real-world constraints, Arm architecture features, and software techniques used to accelerate AI inference—including SIMD (SVE, Neon), low-bit quantization, and the KleidiAI library. Each concept is taught using concise, interactive notebooks and narrated examples, enabling you to measure, tweak, and iterate on actual hardware like the Raspberry Pi 5 or AWS Graviton3 cloud instances.
Getting Started with Machine Learning at the Edge on Arm
Arm (via Coursera)
The age of machine learning has arrived! Arm technology is powering a new generation of connected devices with sophisticated sensors that can collect a vast range of environmental, spatial and audio/visual data. Typically this data is processed in the cloud using advanced machine learning tools that are enabling new applications reshaping the way we work, travel, live and play. To improve efficiency and performance, developers are now looking to analyze this data directly on the source device – usually a microcontroller (we call this ‘the Edge’). But with this approach comes the challenge of implementing machine learning on devices that have constrained computing resources. This is where our course can help! By enrolling in Machine Learning at the Edge on Arm: A Practical Introduction you’ll learn how to train machine learning models and implement them on industry relevant Arm-based microcontrollers. We’ll start your learning journey by taking you through the basics of artificial intelligence , machine learning and machine learning at the edge , and illustrate why businesses now need this technology to be available on connected devices. We’ll then introduce you to the concept of datasets and how to train algorithms using tools like Anaconda and Python. We'll then go on to explore advanced topics in machine learning such as artificial neural networks and computer vision. Along the way, our practical lab exercises will show you how you can address real-world design problems in deploying machine learning applications, such as speech and pattern recognition, as well as image processing, using actual sensor data obtained from the microcontroller. We'll also introduce you to the open source TensorFlow Python library, which is useful in the training and inference of deep neural networks. In the final module you’ll be able to apply what you’ve learned by implementing machine learning algorithms on a dataset of your choice. To be successful in the course, you should h...
Arm AMBA AXI Protocols Overview
Arm (via Coursera)
Introduction to AMBA® AXI is a practical introduction to the AXI (Advanced eXtensible Interface) protocol, a core building block of modern system-on-chip (SoC) designs. This course is designed for learners who want to strengthen their understanding of how data moves within complex computing systems. You’ll explore how AXI enables efficient, high-performance communication between processors, memory, and peripherals, and how protocol behaviour impacts system performance and correctness. Through short, focused videos and assessments, you’ll learn about AXI channels, transactions, ordering rules, atomic accesses, and key protocol features used in real-world designs. The course also includes coverage of recent AXI updates to ensure relevance to current platforms. By the end of the course, you’ll be able to read and reason about AXI transactions with confidence, making this knowledge directly applicable when working with hardware platforms, low-level software, or system architecture documentation.
Teaching AI on the Edge
Arm (via Coursera)
Mobile and edge devices are already able to deploy large language models (LLMs) in artificial intelligence (AI) applications that will have a transformational impact on society. How can academia prepare the next generation of engineers to leverage the opportunities and address the challenges presented by AI on the Edge? In this course, Dr. Catherine Breslin, an AI consultant from Cambridge UK and co-founder of Kingfisher Labs, discusses key considerations when teaching AI in higher education. Teaching AI on the Edge is designed to equip educators and learners with the knowledge and skills to successfully implement artificial intelligence in resource-constrained environments. This course blends essential theoretical foundations with practical project-based experiences, preparing you to understand, build, and effectively teach AI systems optimized for edge devices. You'll explore the evolution from specialized task-specific AI models to versatile multimodal foundation models, learning critical techniques such as pruning, quantization, and small-model design that allow advanced AI capabilities to operate efficiently on limited hardware. The course emphasizes iterative development practices, rigorous model evaluation, and responsible AI deployment, highlighting data privacy, model bias, and regulatory considerations. Throughout this course, you'll gain insights into practical teaching strategies that balance theory and hands-on activities, encouraging creative, inclusive, and collaborative approaches to AI education. You'll also discover how to leverage open-source tools and frameworks to accelerate learning and inspire students to tackle real-world problems through innovative edge AI solutions. Join us to deepen your understanding of AI's potential, master effective teaching practices, and inspire the next generation of AI innovators to positively impact society.
Cortex-M Software Development Fundamentals
Arm (via Coursera)
This course delves into software development topics such as working with Arm C/C++ compilers and Arm debug tools to optimize your software, whether it's for performance or code size. This course also looks more closely at the Common Microcontroller Software Interface Standard and how it helps ensure that software targeted at Cortex-M devices can be written using a consistent approach.
Armv8-M Architecture Fundamentals
Arm (via Coursera)
The course includes fundamental architecture topics that are key to understanding how any Cortex-M processor functions internally. The course focuses specifically on the Armv8-M version of the Arm Architecture, which processors like the Cortex-M33 and Cortex-M55 are based on. However, even if you're working with older processors based on earlier versions of the architecture, like Armv6-M or Armv7-M, a lot of the information is mostly still very relevant and useful.