Embedded computer systems have been around for a while since the release of the first microprocessor that started the small computer device revolution. Some of them, such as digital clocks or calculators, have become so common that we don’t even perceive them as computers anymore. At the same time, technologies now are developing so rapidly that it’s pretty easy to miss something. In this article, we overview the current and emerging trends in embedded software development so that everybody makes sure they’re up to date with this important technology domain of modern life.

What is an Embedded System?

An embedded system is a computer system designed to deliver a specific function and is embedded into a device or is a part of another system. Embedded systems are everywhere around us, from common home devices and appliances such as remote TV controls,  microwave ovens, and digital cameras – to outer-world things that are a part of modern everyday life such as traffic lights, cars, plant manufacturing equipment, and flight control systems in aerospace.

To better understand the opportunities provided by modern embedded technology trends, let’s analyze how an embedded system is built and what it needs to work.

Embedded Hardware

At the heart of any embedded system is a combination of electronic components – transistors, resistors, capacitors, diodes, etc. that make up an electronic circuit. Modern electronic circuits consist of billions of such miniature electronic components inseparably mounted on a small piece of a silicon wafer – an integrated circuit or a chip. Within an embedded system, ICs function as different hardware building blocks: microprocessors, microcontrollers, memory, logic gates, and other elements. The size, growing processing power, and price of ICs greatly enable device miniaturization, affordability for consumers, and functional diversity for engineers and business owners.

Embedded hardware architecture also includes such elements as a user interface – a set of features, buttons, and displays to be used for interaction with the device, a power supply – a battery or power outlet, and communication ports – the means for it to communicate with outer devices, e.g. USB or Ethernet.

Embedded Software

Another side of embedded systems that makes them work is firmware and software. Firmware is a type of software designed for low-level system control and hardware communication. Within an embedded system, it is responsible for providing basic system communication instructions, such as hardware startup, system boot, input and output tasks, or internal communication between system elements. Firmware is written into the system’s non-volatile ROM and is rarely changed or updated.

Embedded software includes applications designed to control the overall system’s functioning on a high level, aka business logic. This includes the operation workflow, data processing, and interaction with other devices connected to the embedded system. Embedded software runs within the system’s main memory (RAM) and can be changed and updated anytime. Embedded software programs are typically more complex than firmware, and some of them may require high processing, memory, and power capacity.

Both embedded firmware and software’s intended function, so we can say that any embedded system is unique and tailor-made for a specific set of business needs and application requirements. The software engineering domain of embedded programming services is dedicated to the design, architecture, and implementation of custom embedded systems, starting with hardware selection to system assembly, coding, software deployment, testing, and continuous support.

Embedded Tech Trends 2024

Let’s consider an extensive and well-advanced use case of using embedded technology – vehicles and transportation. Modern cars are equipped with many kinds of embedded systems that help improve the driver experience as well as overall safety. For example, such feature as adaptive cruise control makes long trips less exhausting for drivers, and advanced multimedia systems provide convenient user interfaces and passenger entertainment. Meanwhile, antilock brakes, traction control, stability control, backup cameras, and other various “assistants” ensure greater safety by enhancing the driver’s skills with technology.

However, we can take the use of embedded systems even further when integrating them with other, innovative and state-of-art technologies. Taking advantage of such innovations in embedded systems as the rise of AI, high-speed wireless connectivity, cloud computing, and impressive hardware evolution, engineers and technology enthusiasts today deliver world-changing embedded solutions.

1. The Transformative Impact of AI

The term Artificial Intelligence covers a range of knowledge areas that deal with tasks related to making machines intelligent. By “intelligent” we mean the ability to perceive the world and process information in a way similar to human thinking. This includes such capabilities as computer vision, natural language processing (NLP), analytics, decision-making, forecasting, content generation, and more.

The use of AI in embedded systems is definitely the leader of embedded technology trends these days as this provides a number of advantages.

• Truly Smart Functionality

To make a device or system able to identify and classify objects, understand human speech, or generate unique responses using human language, engineers work with big data sets and complex algorithms called Machine Learning models and Neural Networks. They train the ML models and NNs to complete specific AI tasks using dedicated data sets and then integrate them into a computer system.

Such intelligent embedded systems need special-purpose processing units that are powerful enough and optimized for handling AI workloads that require parallel computing capabilities. They are typically more expensive than common ones but make a huge difference in performance. Hardware design with energy efficiency, adequate memory, and power consumption in mind is also critical. On the other side, careful ML needs analysis and model optimization techniques can help reduce the model complexity and lower resource requirements.

• Real-Time Response

Artificial Intelligence empowers devices with smart functions, keeping them independent from the network connection at the same time as they don’t need to share data with any external processing center to provide value – everything is done on the device level. This drastically increases system response time, making real-time ML processing on embedded systems a true breakthrough.

• Increased Security and Applicability

Additionally, no need to share data over the network means less chance of system breaches leading to sensitive user data leaks. Also, as embedded systems take a variety of sizes, forms, and shapes, integrating them with AI technology creates limitless new possibilities and applications.

2. Multicore Microprocessors and Microcontrollers

Rapid semiconductor evolution leads to the development of powerful yet small-sized multicore CPUs, hardware accelerators (GPUs, VPUs, etc.), and microcontrollers. As one of the top embedded systems trends, this makes a great leap for embedded systems toward even more applications and possibilities imaginable.

• AI Capabilities

As mentioned in the previous section,  Artificial Intelligence is impossible in embedded systems without hardware capable of parallel processing. Due to their multiple cores, multicore microprocessors and microcontrollers enable embedded systems to handle complex computing tasks. These include data intelligence and analytics, decision-making, object detection, speech recognition, and other AI workloads.

• Task Execution Speed

Unlike sequential processing, multicore processors and hardware accelerators also enable concurrent processing, which means breaking down tasks into separate threads that are executed simultaneously. This results in much faster execution and improved overall system performance, which is particularly important for real-time embedded systems.

• Energy Efficiency

Parallel processing using multiple cores also allows for reduced energy consumption and device overheating. As multicore processors yield more processing power at lower clock frequencies, they need less energy and emit less heat than a single-core processor to perform the same task. This characteristic is critical for battery-powered embedded systems whose core requirement is energy-efficient design.

3. Wireless Connectivity Advancements

We know that embedded systems do not necessarily need internet connectivity to function and bring value. But as soon as we add such an option to an embedded system, it becomes a connected device or a part of the IoT ecosystem. As one of the most crucial embedded software development trends, this is particularly useful when we need to keep track of or control a device or a cluster of devices remotely, such as in a smart home.

An IoT system consists of a central data processing unit, which can be hosted in a cloud infrastructure or on a local server, a physical,  desktop, web, or mobile user interface, and a network of devices connected to it over the network. Connected devices stay online due to a built-in wireless connectivity module. It can be a SIM, Wi-Fi, radio, or other module that makes sure data travels to the central unit over a wireless network.

• Internet Connectivity

Last-generation wireless long-distance cellular connectivity technologies, such as 4G LTE, 5G, and 6G projected to launch in 2030  provide the highest bandwidth and data transfer speed possible today, can support hundreds of thousands of simultaneous connections, and ensure reduced latency for data transfers. Wi-Fi 6 and 6E are the short-range connectivity protocols to be used in embedded devices to ensure high data travel speed and bandwidth within a local network (such as in smart home applications). Considering the ever-growing amount of sensors and data that travel from these sensors to the central processing unit, the need for connectivity capabilities will be growing.

• Low-Power Networks

There are also Low-Power Wide-Area Networks (LPWAN) that provide long-range communication with low-power devices, such as LoRaWAN (Long-Range WAN), NB-IoT (Narrowband IoT), and Sigfox. NB-IoT is cellular-based while LoRa and Sigfox technologies use radio waves for communication and thus provide low bandwidth for data transfers. Their advantages include long-range (exceeding the range of cellular networks), low cost,  and high precision for location services. They also provide end-to-end data security and scalability opportunities.

• Satellite Connectivity

Satellite wireless connectivity technologies are one more way for connected devices to exchange data with each other and the central data processing unit. The great advantage of satellite communication is global coverage, especially in areas where terrestrial networks are not available, as well as the support of high data rates and high mobility of users and devices. However, satellite connectivity cannot ensure low latency and requires high investment and maintenance costs.

4. Edge Computing

Unlike connected devices in IoT, edge computing does not require constant connectivity with a centralized data and processing center. An edge computing system roughly consists of three core units:

• an edge device that collects data using sensors or other input methods and transmits it further for processing. Edge devices may also have some basic data preprocessing functionality, such as data filtering, aggregation, cleaning, compression, etc.
• An edge node is a control panel or app that serves as a gateway or micro-data center and provides localized data processing, storage, support for ML algorithms, and other core system functionality. It is located close to the edge device and is usually connected via short-range high-speed data connectivity, such as Bluetooth, or by sharing the same internal network. The nodes exchange data in real or near-real time.
• a central unit for data processing and storage that keeps in sync with the edge nodes but does not constantly connect with them. This system element is designed to store and handle large amounts of data and perform some bulky and non-time-critical analytic tasks.

How is this beneficial for embedded systems?

• Response Speed

Similarly to embedded ML, edge computing solutions help to reduce data latency and increase system responsiveness, bringing data processing closer to data generation. As data has a shorter distance to travel to the core processing unit, the system’s response will be near-real or real-time, which is one of the main requirements for modern embedded systems.

• Reliability

Systems based on edge computing are relatively autonomous and independent of the connection quality, central server, and possible failures, as the core functionality relies on the edge nodes. As a result, the system’s work is more stable and reliable. Also, this means more data security as it, or a part of it, does not get shared over the external network and thus cannot be accessed by unintended parties or malicious actors.

5. Other Innovations in Embedded Software Development

Advancements in RTOS

Real-time operation is one of the most frequent needs in embedded systems. Thus, the embedded software industry greatly benefits from RTOS (Real-Time Operating Systems) – an OS type designed specifically for embedded systems with real-time requirements. An RTOS is designed to handle data in a deterministic and predictable way, with strict timing in mind, ensuring the system prioritizes real-time execution instead of multitasking, for example. This follows the first-in-first-out processing principle, creating specialized data structures, such as priority queues and circular buffers, and using real-time scheduling algorithms.

Given their critical importance to embedded systems development, RTOS widely integrates with other technology domains relevant to embedded systems. These include the adoption of RTOS in IoT devices, AI-powered embedded systems, and edge computing. In turn, this and the way RTOS deals with data pushes the need to strengthen the security features in RTOS, such as data encryption, access control, secure boot, authorization, and virtualization.

Augmented Reality and Virtual Reality Integration

Another major trend for embedded systems evolution is the growing interest in the integration of AR and VR into wearables, such as glasses, to enhance the users’ sensory experiences and provide contextually relevant information to them in real time. Today this finds interesting applications in gaming, visual prototyping, and training scenarios.

AR and VR integration, however, requires powerful hardware and efficient software algorithms that will enable real-time processing and satisfy the rendering demands of AR and VR applications. Also, the interactive nature of these technologies requires the implementation of impeccable sensor fusion, precise tracking, depth sensing, and gesture recognition. Resource-constrained wearable embedded systems, which need to be power-efficient, lightweight, ergonomic, and safe at the same time, are posing a challenge.

High-Level Programming Languages

High-level programming languages offer strong abstraction from the machine language. For software developers, such languages provide enhanced productivity, as they simplify and automate many programming tasks and come with many ready-to-use developer tools, libraries, and frameworks.

Programming languages such as C++, Python, Go, Ada, and Rust are gaining popularity among embedded software developers due to their features that help achieve optimal performance with constrained resources. These include C++’s low-level control, safe concurrency with Rust and Go, strong typing and extensive compile-time checks with Ada, and Python’s versatility.

Use Cases and Examples

In this section, we’ll review some popular and useful applications that best reflect the modern embedded software trends and innovations.

Smart Home

In Smart Home applications, devices are connected to the network via Wi-Fi connectivity and thus can be controlled remotely and send signals and notifications to users in near-real or real-time through a user interface on mobile and desktop devices. The data collected by these devices can be processed and stored for a certain period in the service provider’s cloud storage, which users can also access from their UI. Thus, due to the IoT technology, users may access their home’s surveillance system at any time and from any location, launch the robotic vacuum cleaner, start doing laundry, check up on the fridge food supply, or feed a pet.

AI and edge computing are also applicable in this domain, granting more autonomy to the system. For example, the Ajax Systems company provides automated and integrated security, fire safety, water leak prevention,  video surveillance, and home comfort systems, offering automation scenarios and scheduling that won’t require the user’s real-time control.

Automotive Industry

In the automotive industry, the fusion of embedded systems and AI brought to reality what we know as autonomous vehicles. Thanks to the implementation of computer vision and decision-making algorithms, cars are able to identify and classify obstacles to avoid collision, detect signs, pedestrians, and other traffic participants, and navigate safely within a constantly changing environment.

Smart City

Urban space organization is becoming more advanced with the use of modern innovations, such as AI and high-speed connectivity. Since many systems we can see in city streets, such as surveillance cameras, traffic lights, energy grids, and transport are embedded systems on their own, powering them with data processing capabilities brings a lot of benefits.

For example, by using smart transportation systems, it is possible to reduce traffic congestion and emissions and improve public transport routes for the commuters’ convenience. Smart energy solutions use sensors and software to collect and analyze data about the electric grid for predictive asset optimization, upstream and midstream operations spotting underperforming and overconsuming equipment, and improved energy consumption.

Manufacturing and Industry 4.0

The IoT technology is widely used for fleet management and maintenance of vehicles or machinery in transportation, logistics, warehouses, and manufacturing. Equipped with connected GPS trackers, as well as vibration, humidity, temperature, and other sensors, a large number of machines and devices can be observed remotely. This allows the system to collect, store, and analyze various data within the central processing unit, providing business owners with sorted-out information and insights about the state, location, performance, potential issues, and other fleet characteristics.

Meanwhile, intelligent embedded systems for manufacturing and fleet management enable predictive maintenance: by collecting and analyzing data about the behavior of the device or its elements, AI algorithms can let you know when it’s time to renew some mechanism parts.

Healthcare

In healthcare, some of the typical use cases of embedded systems include AI-powered or IoT-based patient wearables that can track patient vitals, spot disturbing patterns, alert users and their doctors, or even provide guided advice. However, large-scale devices, such as magnetic resonance imaging (MRI) and computer tomography (CT) machines also contain advanced embedded systems, as well as other medical equipment does: defibrillators, blood pressure monitoring devices, digital flow sensors, and foetal heart monitoring machines.

Future Outlook and Conclusion

We’ve overviewed the top trends and innovations in modern embedded systems development. With today’s need for rapid and extensive data exchange between the world, humans, and computer systems, the future of embedded software seems to be focused on further improving data transfer speed and processing capabilities. These two aspects actively drive the development of powerful microprocessors and diverse sensors, as well as the connectivity channel advancement and diversification.

Data privacy and security should be another important aspect of embedded systems innovations, finding themselves at the intersection of software technologies and governmental efforts. This statement is also relevant to the use of AI technologies and some ethical considerations regarding the level of autonomy and independence they should provide to computer systems.