September 03, 2024
Having spent significant time in the realm of embedded systems and IoT development, I’ve consistently encountered challenges related to initial setup and scaling to production. Chip selection, a pivotal aspect of this process, often involves meticulous shortlisting of microcontrollers and architectures, followed by the acquisition of development boards and prototyping to identify the most suitable chipset. Not to mention the lead times and chip shortage problems, this is merely the beginning.
Once this initial step is complete, the journey extends to establishing IDEs, debuggers, test environments, and other development, testing, collaborating and shipping tools. This process can be arduous, difficult to scale, and often discouraging. Yet, as those familiar with my work know, I’ve consistently been vocal about the bridging of the gap between hardware/IoT/embedded and software/cloud development, thanks to new product lifecycle management workflows and DevOps practises.
Cloud developers have long enjoyed the luxury of robust tools and streamlined software lifecycles. The ability to scale from a single server instance to thousands with a simple click, facilitated by Docker Containers, Kubernetes, and DevOps workflows like CI/CD, has been a major draw for embedded developers.
One such innovation that brings IoT developers closer to this cloud-native model is Arm’s Virtual Hardware on the cloud. Let’s talk more about that in this blog and see how it fits into the bigger picture starting from:
Developing for embedded and IoT applications involves numerous challenges, including:
Arm Virtual Hardware addresses these challenges by providing a virtualised environment where developers can simulate and test embedded and IoT applications without relying on physical hardware. This offers several key benefits:
Running ML models on the Edge compute devices is one of the most common applications where Arm-based processors are deployed today. Think of smart speakers, phones, traffic lights, cameras, etc. These products and applications can benefit greatly by adopting Arm virtual hardware in prototyping and testing life cycles.
Machine Learning Operations (MLOps) involves managing the entire lifecycle of machine learning models, from development to deployment. Using Arm virtual hardware, developers and data scientists can test their model on almost a real Arm processor, estimating the performance of different architectures and chipsets; this enables them to pick the best hardware suitable for their models, saving a lot of cost and time in development and bringing product to market. Developers can train machine learning models on virtualised Arm hardware, ensuring compatibility with target devices and architecture, enabling
Arm Virtual Hardware is a game-changer for embedded and IoT development. By addressing the challenges of traditional development methods, AVH enables faster time to market, improved collaboration, and enhanced flexibility. As the adoption of embedded and IoT devices grows, AVH will play a vital role in driving innovation and efficiency.
In the next blog, we will cover how to use Arm virtual hardware, but meanwhile, please help us with our new Developer Nation 25th survey …….. <UTM URL AND CTA>