Modern Embedded System Software: Best Combinations for Performance and Flexibility

As embedded systems continue to power everything from consumer electronics and automotive controllers to medical devices and industrial IoT nodes, the demand for high performance and design flexibility has never been greater. Today’s embedded platforms must juggle real-time responsiveness, power efficiency, connectivity, security, and rapid development cycles — all within tight resource constraints. Choosing the right combination of software — including real-time operating systems (RTOS), Linux-based distributions, middleware, drivers, and development tools — is crucial to building scalable and future-proof solutions. In this article, we explore the most effective software combinations for modern embedded systems, helping engineers strike the ideal balance between performance, flexibility, and maintainability.

Best Software Combinations for Performance and Flexibility

In today’s embedded landscape, systems are powering increasingly complex devices: from industrial automation and connected vehicles to smart medical devices and AR/VR-enabled wearables. As the demands on these platforms grow, so too does the importance of selecting the right software combination to balance real-time constraints, performance needs, and long-term maintainability. This article focuses on one crucial decision: choosing the appropriate programming language(s) and architecture for the embedded software. Specifically, we compare Assembly, C, and C++ with object-oriented design (OOD), examining their roles, trade-offs, and relevance in modern, performance-driven, and AI/AR-compatible systems.

Language Profiles Overview

Assembly Language

• Low-level, hardware-specific language offering direct access to registers and memory.
• Ideal for fine-tuned performance but extremely difficult to maintain or scale.

C Language

• Procedural programming language with close-to-the-metal efficiency.
• The de facto standard in embedded development due to its balance of control, portability, and speed.

C++ with Object-Oriented Design (OOD)

• High-level language enabling abstraction, encapsulation, and code reuse.
• Increasingly popular in complex embedded applications involving AI, ML, and multimedia.

Pros and Cons

Use Case Scenarios

Use Assembly When:

• Writing bootloaders or startup code.
• Implementing ultra-fast interrupt service routines.
• Tuning performance-critical code (e.g., encryption, DSP).

Use C When:

• Developing device drivers or HALs.
• Implementing system-level code within an RTOS.
• Ensuring tight control over memory and timing.

Use C++ (OOD) When:

• Building scalable applications with reusable components.
• Working on edge AI models, vision systems, or AR/VR frameworks.
• Leveraging middleware, protocol stacks, or real-time communication.

Blended Approach

Most modern embedded systems benefit from a hybrid approach:

• Assembly is used sparingly for optimization hotspots.
• C forms the foundation for RTOS integration, hardware drivers, and low-level logic.
• C++ takes over in application logic, user interaction, AI inference, and modular architecture.

This layered strategy ensures performance where it matters, without sacrificing maintainability and scalability.

Recommended Approach

The best approach is to use the blended approach indicated above. Assembly languages has to be used during early rebooting and hardware setup phase. C can be used after the memory block is initialized and stack frame is setup. After that, you are free to chose between C and C++ language. In an practical way, it is very difficult to do everything in C++ for modern embedded system due to many 3rd party solutions are written in C only, e.g. Zephyr RTOS, although C++ is supported by it.

In other point of view, some embedded systems are totally designed using C language, which is fine but missed a lot of ways to take advantage of modern C++ ( C++14 afterward especially) can provide. C++’s OOD design methodology along with idioms such as RALL, Rule of five, STL library and new language features make your design extensible, manageable and less error-prone. Take the need to make sure data structure are aligned correctly to avoid exception as an example which is demoed by following code snippet. It is not easy to get such kind of easy understandable, portable code with C language.

#include <iostream>
#include <cstdlib>     // std::aligned_alloc, std::free
#include <new>         // placement new
#include <cstddef>     // std::max_align_t

struct alignas(32) MyAlignedType {
    int data[4];
    MyAlignedType() { std::cout << "Constructed\n"; }
    ~MyAlignedType() { std::cout << "Destroyed\n"; }
};

int main() {
    using T = MyAlignedType;

    std::size_t size = sizeof(T);
    std::size_t alignment = alignof(T);

    void* raw = std::aligned_alloc(alignment, size); // ensure alignment

    if (!raw) {
        throw std::bad_alloc();
    }

    T* obj = new(raw) T(); // placement new

    obj->~T();             // manually destroy
    std::free(raw);        // free memory

    return 0;
}

assert(reinterpret_cast<uintptr_t>(raw) % alignof(T) == 0);

template <typename T>
T* allocate_aligned() {
    void* mem = std::aligned_alloc(alignof(T), sizeof(T));
    if (!mem) throw std::bad_alloc();
    return new(mem) T;
}

template <typename T>
void destroy_aligned(T* obj) {
    obj->~T();
    std::free(obj);
}

Other language features, such as template, smart pointer make your code more dense, less possible of memory leaking, expandable to different data type with less errors.

A common algorithm rarely needs to be reinvented and programed from scratch because the algorithm is probably available in the STL. In addition, the STL authors have diligently optimized it and tested it. Using the STL throughout the project, therefore, results in a more legible, efficient and portable body of source code, automatically. In addition, other developers will find it easy to analyze and review source code that uses the STL because the standardized template interface encourages consistent style and reinforces coding clarity.

Lambda expressions offer the compiler more opportunities to optimize by making the function, its iterator range and its parameters visible to the compiler within a single block of code. In this way, the compiler has access to richer set of register combinations, merge possibilities, etc. and it can do a better optimization. Using lambda expressions consistently throughout an entire project can save significant code and generally improve the performance of the whole software. Templates expose all of their code, their template parameters, function calls, program loops, etc. to compiler optimization at compile time. This provides the compiler with a wealth of information allowing for many intricate optimizations such as constant folding and loop unrolling. Using templates can result in many (sometimes subtle) improvements in runtime performance. Templates provide for scalability, allowing the scale and complexity of a particular calculation to be adjusted by changing the template parameters. compile-time constants are just as efficient as preprocessor #defines in C language, but have superior type information. Compile-time constants are well-suited for defining register addresses because they require no storage and are available for constant folding. Register addresses defined as compile-time constants can also be used as parameters in C++ templates.

This layered strategy ensures performance where it matters, without sacrificing maintainability and scalability.

Hardware Acceleration & Language Implications

Besides software and language considerations, modern embedded system combined both hardware features and software solutions tightly to achieve the overall goals.

Modern SoCs often include:

• DSPs, NPUs, and TPUs for AI/ML acceleration.
• ISPs and GPUs for image processing and AR/VR.

C++ is better equipped to interface with these accelerators via SDKs (e.g., TensorRT, OpenCV, OpenXR) and hardware abstraction layers. Toolchains now support C++17/20 features optimized for embedded environments (e.g., static polymorphism, constexpr, embedded STL).

RTOS Compatibility

RTOS selection is equal important for high performance embedded system design. Here is a list of commonly available RTOS solutions for language supporting point of views:

Final Thoughts

No single language or architecture fits every embedded system. The best results often come from mixing Assembly, C, and C++ — each used where it makes the most sense. For maximum performance, minimal footprint, and long-term flexibility:

• Use Assembly for critical optimizations.
• Use C for low-level, RTOS-integrated logic.
• Use C++ (OOD) for high-level architecture, especially when dealing with AI, AR/VR, or modular expansion.

By combining these layers with modern RTOS support and hardware acceleration, developers can create robust, future-ready embedded platforms that meet today’s demands without sacrificing tomorrow’s adaptability. Let’s dive into an special embedded application use case to evaluate which RTOS is best suitable for it, i.e. AI application oriented embedded system as the last illustration of what we have discussed so far.

Choosing the Right RTOS for AI-Oriented Embedded Applications

Why AI Needs a Carefully Selected RTOS

AI applications—such as edge vision inference, voice recognition, predictive maintenance, FSD driving, or AR/VR tracking—introduce unique demands on embedded systems:

• Heavy use of parallel compute (DSP/NPU/TPU) and memory bandwidth.
• Low-latency and deterministic response.
• Multi-threaded data pipelines (camera, inference, UI, networking).
• Real-time constraints with limited power and memory.
• Hardware acceleration SDK integration (e.g., CMSIS-NN, TensorFlow Lite Micro).

Key Features required in an RTOS for AI Applications

• Multithreading & SMP support for parallel inference tasks.
• Low-latency deterministic scheduling to meet real-time needs.
• Hardware SDK compatibility for NPUs, DSPs, and GPUs.
• POSIX and C++ support for easy framework integration.
• Fast boot & small footprint for edge devices.
• Secure I/O and model handling via trusted memory regions.

Top RTOS Candidates for AI-Centric Applications

Example: RTOS Selection for Real-World AI Use Case

• Use Case: Real-time smart camera with CNN inference.
• Requirements: Frame buffering, inference (CMSIS-NN or TFLM), network + secure OTA.
• Best Match: Zephyr or QNX for full AI/vision support, security, and modularity.

Reasons Considered to Choose RTOS

• MCU vs MPU? : FreeRTOS/Zephyr vs. VxWorks/QNX.
• Need for real-time AI response?: Choose preemptive RTOS with low-latency scheduling.
• Hardware accelerator SDK available?: Match RTOS to vendor tools.
• Cloud-connected or OTA model updates?: Look for modular networking and TLS (e.g., Zephyr, Azure RTOS).
• Safety or certification required? : Use QNX, SafeRTOS, or VxWorks.

With those careful selection, embedded developers can unlock high performance AI at the edge while maintaining real-time predictability and system safety

Sample Architecture Diagram for AI-Centric RTOS-based System

+----------------------------+
|   AI/AR Application Layer  |
|  - Object Detection        |
|  - Pose Estimation         |
+-------------+--------------+
              |
+-------------v--------------+
|     C++ AI Inference       |
|  (TensorFlow Lite Micro)   |
+-------------+--------------+
              |
+-------------v--------------+
|       RTOS Kernel Layer    |
|  - Thread Scheduler        |
|  - IPC / Messaging         |
|  - Device Drivers          |
+-------------+--------------+
              |
+-------------v--------------+
|   HAL + Hardware SDKs      |
|  - NPU/DSP Drivers         |
|  - Camera / Display / GPIO |
+-------------+--------------+
              |
+-------------v--------------+
|       MCU / MPU SoC        |
+----------------------------+

Sample Code Snippet (RTOS + AI Inference Loop)

#include "tensorflow/lite/micro/micro_interpreter.h"
#include "FreeRTOS.h"
#include "task.h"

void ai_task(void* pvParameters) {
    // Load model and allocate tensors
    tflite::MicroInterpreter* interpreter = LoadMyModel();

    while (true) {
        CaptureImage();                // From camera sensor
        PreprocessImage();             // Normalize, resize, etc.
        interpreter->Invoke();         // Run inference
        ProcessResults();              // Action based on output
        vTaskDelay(pdMS_TO_TICKS(50)); // Run every 50ms
    }
}

int main() {
    xTaskCreate(ai_task, "AI_Task", 2048, NULL, 2, NULL);
    vTaskStartScheduler();
    while (1); // Should never reach
}

Conclusion

Modern embedded systems are evolving rapidly, driven by the demands of real-time AI, AR/VR, and connected intelligence. To meet these challenges, developers must balance low-level performance with high-level modularity by selecting the right combination of Assembly, C, and C++ — along with a capable RTOS tailored for modern AI,AR/VR workloads. Whether you’re building a smart sensor node, a robotic controller, or a wearable vision device, understanding how to map language features and RTOS capabilities to your hardware accelerators and application logic is key. By leveraging the right tools and architecture from the start, you can create systems that are not only efficient and scalable but also ready for the future of embedded intelligence.