Embedded Software Services

Embedded software is a piece of software that is implanted in hardware or non-PC devices. Written specifically for the particular hardware that it runs on, it usually has processing and memory restrictions because of the device’s limited computing capabilities. Embedded software is becoming more and more important throughout industries, and can be found in a range of solutions, including GPS devices used in the automotive sector, factory robots for various types of manufacturing tasks, calculators and even smartwatches.

Real-Time Operating System (RTOS) is an operating system designed to manage and control the hardware and software resources of an embedded system in real time. Unlike general-purpose operating systems (Windows or Linux), which are optimized for a wide range of applications and can have variable response times, a RTOS is specifically designed to provide predictable and deterministic execution of tasks. The main characteristic of this system is to execute code within the specified deadline. RTOS is crucial for systems where timing, reliability and real-time performance are essential. The main features include predictable and deterministic behavior, task scheduling, low latency, interrupt handling, resource management and communication and synchronization for inter-task communication. Common examples of RTOS are VxWorks, FreeRTOS, Zephyr, ThreadX and Nucleus RTOS.

Bare machine (or bare metal) refers to a computer executing instructions directly on hardware without an intervening operating system. Today it is mostly applicable to embedded systems and firmware with time-critical latency requirements, while conventional programs are run by a runtime system overlaid on an operating system. One key characteristic is that there is no operating system – developers are responsible for managing all hardware resources, scheduling tasks and handling low-level operations. Bare metal programming enables full control over a system's resources, including the CPU, memory and peripherals. This level of control allows for efficient resource utilization but also demands a deep understanding of the hardware. Bare metal programming has a smaller memory and processing footprint, which is advantageous in resource-constrained embedded systems. It also enables customization, since developers can tailor their software to the specific requirements of the hardware and application, which result in optimized performance. A final feature is a lack of portability, since code developed for one hardware platform may not be directly portable to another due to the hardware-specific nature of bare metal programming. Common use cases for bare metal programming include microcontroller-based systems, embedded systems, firmware development for hardware devices and applications where minimal latency and resource efficiency are critical. While it offers significant advantages in terms of control and performance, it also comes with the responsibility of handling many low-level details that are abstracted away when using an operating system.

The EU’s CRA will impose obligatory security requirements for the entire life cycle of hardware and software products to better protect businesses and consumers. It aims to make the security of products with digital elements more transparent and reduce vulnerabilities of solutions available in the EU market. To do that, the CRA places the responsibility for a product’s cybersecurity throughout its entire life cycle on manufacturers. The CRA means that manufactures will have to take security into account at all stages of product life cycle, from planning to maintenance, enable software updates for at least five years, ensure that vulnerabilities are effectively addressed throughout a sold product’s expected lifetime or for five years and document all cybersecurity risks.

Porting embedded software to a new microcontroller unit (MCU) may be required for several reasons, often driven by hardware or business considerations. Take for example hardware upgrades or changes. If new hardware features, better performance, or different peripherals are required, you may need to port your software to a new MCU that supports these features. There’s also the issue of cost reduction, since porting software to a new MCU enables you to leverage the lower-priced hardware while maintaining the same functionality. Another reason might be component availability, like when the original MCU or components used in your product become barely available on the market due to global shortages or due to end-of-life (EOL) status. Companies also need to consider security and compliance as regards meeting new security standards or requirements like the CRA or Data Act, you may need to port your software to an MCU that provides better security features and compliance options. If power efficiency is a concern, you may want to port your software to an MCU with lower power consumption characteristics, especially in battery-powered or energy-efficient devices. Finally, connectivity and scalability might require porting embedded software when there is a need for new options, such as Wi-Fi, Bluetooth, or additional communication interfaces or to increase or decrease capacity based on demand and business goals.

The data act which will change how data is used in the EU’s economic sectors. This new regulation will give businesses and consumers more control over data generated by their IoT devices, allowing them to use and share this data in more ways than before. This move is meant to create new market opportunities, foster innovation and efficiency through data-driven insights and enable device users to repair their connected devices for lower prices. The Data Act will empower companies to develop new products and enhance existing offerings, but wider data accessibility will also mean businesses need to pay extra attention to data safety.

HIL testing combines real hardware components with computer-based simulations to create a virtual environment that replicates the real-world conditions of the tested system. HIL testing also enables engineers to evaluate a system without the complete hardware available, especially during the early stages of device development, by using a real-time simulator that acts as a digital twin of the existing hardware system or parts of it. During HIL testing, the simulation system connects hardware components and mimics the behavior of the missing components, like the physical environment or interconnected systems. The level of detail in the simulated environment varies from simple models to highly detailed representations, depending on the complexity of the tested system. Interacting with the hardware components in real time, the simulation system makes it possible to send signals and test received responses. To confirm functionalities operate as intended and to identify a solution’s security vulnerabilities, test the expected behavior as if it were the actual system or check the negative scenarios.

Embedded systems specialists are increasingly turning to HIL testing for a number of reasons. One reason is smarter testing, since introducing tests at early stages of development and maintaining complete control over conducted test conditions leads to better results. Another reason is cost reduction, as identifying potential problems quicker in embedded systems is essential to mitigating the risks of high costs related to producing additional, expensive prototypes or advanced field testing. There’s also the value of improved security, because using HIL testing at early stages of development lifecycles and designing devices according to cybersecurity standards (CRA, PSA-certified), ensure solutions can withstand cyber threats. All these advantages translate into a competitive edge – exemplary HIL implementation leads to more efficient testing which delivers a superior product faster to the market.

AI-enabled embedded systems refer to embedded devices that incorporate artificial intelligence (AI) capabilities to perform tasks such as data analysis, pattern recognition, decision-making and automation. Embedded AI is cheaper, and more energy-efficient compared to cloud-based AI. The market for AI chips that work at the "edge", which means closer to where data is collected, is growing much faster than the general chip market. Creating embedded AI requires more than just knowledge in traditional embedded systems, data science, and machine learning. It also needs expertise in how devices and sensors work, as well as the ability to process data like videos, audio, or motion quickly. You also need specialized software tools and frameworks to build embedded AI applications. Such systems leverage AI algorithms and machine learning techniques to process data, make inferences and adapt to changing conditions.