Artificial Intelligence, or AI as we popularly know it, has stepped out of the science fiction movies and has become a part of our everyday lives. AI tech is now powering almost everything…from our smartphones to our retail and banking experiences. With the world becoming increasingly software-defined, and with the focus on highly intuitive and contextual experiences increasing, AI becomes a critical technology to adopt. And the software development world has been listening.

According to a QY Research, the AI market size is expected to skyrocket to  $78 billion in 2025 up from $2.65 billion in 2017.

As software begins to get more intuitive, it calls for a certain amount of expertise to be built into the systems programming as well. It is the embedded software that creates the base architecture for the hardware to interface with the programmer and the user to effectively execute the application software on the computer system.

If the very nature of the software is going to change, then clearly, the firmware and the OS, the assemblers, interpreters and schedulers, the loaders, and the linkers too need to get a similar upgrade. After all, a Mercedes Benz C class cannot run on the hardware and software running a Volvo.

That would suggest that it is time for embedded systems developers to pay attention to AI. As applications are becoming increasingly complex and real-time, there is a greater amount of strain on the embedded system powering it. The embedded software too must have the same capabilities of the application otherwise the software will not deliver on its business promise of speed, agility, and intuitiveness. For example, say you are creating a program to recognize cats. Traditionally this can be done by programming in explicit rules such as cats have pointy ears or cats are furry. But what if the program reads these parameters and throws up the picture of a tiger?

With AI technology, this error can be eliminated as the program then sifts through data (pictures of thousands of cats) to find the right pattern and to connect the dots. Here the program needs the right kind of data and large volumes of it. With AI technologies, this task is managed easily. What embedded systems developers thus need to create the right underlying system is a library specifically designed to implement neural networks/ Machine Learning algorithms to improve the execution efficiency according to the required performance benchmarks.

AI in embedded systems also aids the device intelligence that we are so getting used to. AI technologies help in applying such intelligence into the embedded system easily. To traditionally implement this kind of intelligence into the embedded system would be a near-impossible task.

Using AI technologies such as neural networks developers can easily learn and understand the system and application behavior and identify at which point the behavior decays or an ‘upset’ occurs.

As applications get smarter, the embedded system accompanying it also has big expectations to meet. As systems programming is usually done in a low-level language for direct control over memory access, these codes are close to the hardware level. They have to deal with the data transfers across components and also have to deal with memory allocations and registers. The embedded systems programming also has to shoulder the responsibility of enhancing and extending the functions of an operating system.

This demands the efficient handling of hardware resources such as file access, I/O operations, device management, storage management etc. Programming all this in assembly language can be immensely time-consuming, especially as the applications become more complex in nature. Programming in assembly language can also lead to errors and can lead to less-optimal implementations.

By using technologies such as AI, embedded systems programmers can increase the development speed by exploring different scenarios faster and also through automated code generation.

AI technologies also assist the testing needs of embedded systems developers. Given that systems programming has to live with the absence of an abstraction layer and has access to limited programming facilities, they have to pay extra attention to debugging requirements of the application. With AI, developers can increase their testing comprehensiveness by adding a layer of intelligence to test automation.

This could help to address security and other vulnerabilities that could impact the application. This will go a long way in ensuring that the system will provide the necessary service to the hardware.

It won’t be wrong to say that the embedded system is the backbone of any high-performing application and plays a crucial role in the application performance.  As we have seen, with technologies such as AI, these systems can become smarter and easier to develop.

As application infrastructures become more intelligent it may become imperative for software development organizations to complement this with a strong systems programming model backed by the same futuristic technologies that drive these applications.