Comparing Embedded System and Edge AI system Architectures-Part 1

Embedded systems have been an integral part of our lives for decades, powering a wide range of devices from household appliances to industrial machinery. These systems are designed to perform specific tasks with high reliability and efficiency, often operating within constrained environments. On the other hand, edge AI systems represent a more recent innovation, leveraging the power of artificial intelligence (AI) to process and analyze data at the edge of the network, closer to the source of data generation.

In the rapidly evolving technological landscape, two distinct architectures have emerged as pivotal players: embedded systems and edge AI systems. While both share the common goal of enabling intelligent and efficient computing solutions, they differ in their design, capabilities, and applications. This article aims to unravel the intricacies of these architectures, exploring their hardware and software components, and shedding light on the architectural distinctions that set them apart.

As we delve deeper into the realm of embedded and edge AI systems, we will uncover the intricate details that make each architecture unique, while also appreciating the synergies that exist between them. By understanding these architectures, we can unlock new possibilities for innovation, optimization, and seamless integration of intelligent systems into our daily lives. In the first of the two-part article, we will explore the hardware and software architecture of embedded systems. The second one will cover the edge AI systems in detail.

Hardware Architecture of Embedded System

The hardware components in an embedded system are carefully selected and integrated to meet the specific requirements of the target application, balancing factors such as performance, power consumption, and cost. Some of the core components are:

1. CPU: Embedded systems are built around a central processing unit (CPU), which serves as the brain of the system. This CPU is typically chosen based on factors such as power consumption, performance requirements, and cost considerations. Microcontrollers and system-on-chip (SoC) designs are commonly employed in embedded systems due to their compact size, low power consumption, and integration of peripheral components.
2. GPU: As a specialized computing element designed for high-speed image and graphics processing. GPUs excel at performing the same operation on multiple data points simultaneously. They are leveraged primarily for graphics rendering in embedded systems.
3. Input/Output (I/O) interfaces: These interfaces facilitate communication with external devices, enabling data exchange and control signals. Examples include serial ports, Universal Serial Bus (USB) ports, and general-purpose input/output (GPIO) pins.
4. Memory components: Embedded systems typically utilize both volatile memory (e.g., RAM) for temporary data storage and non-volatile memory (e.g., flash memory or EEPROM) for permanent storage of program code and configuration data.
5. Sensors and actuators: Depending on the application, embedded systems may integrate sensors for gathering environmental data (e.g., temperature, pressure, or motion) and actuators for controlling physical processes (e.g., motors, valves, or displays).
6. Communication modules: Wireless communication capabilities, such as Wi-Fi, Bluetooth, or cellular connectivity, are often incorporated to enable data exchange with remote systems or the cloud.

Software Architecture of Embedded System

The software architecture of an embedded system is designed to efficiently manage the available hardware resources and provide the necessary functionality for the target application. It typically consists of the following layers:

1. Real-Time Operating System (RTOS)/OS: Many embedded systems require real-time responsiveness, and an RTOS is employed to ensure deterministic behavior and predictable execution times. Popular RTOS options include FreeRTOS, VxWorks, and QNX. Some larger embedded systems are powered by monolithic OS’s like Linux, Android etc.
2. Device drivers: These software components provide a standardized interface for interacting with the various hardware peripherals, abstracting the low-level details and enabling efficient communication between the application software and the hardware.
3. Middleware: Middleware components, such as communication stacks, file systems, and data management libraries, facilitate the development of robust and modular applications by providing reusable services and abstractions.
4. Application logic: This layer encompasses the core functionality of the embedded system, implementing the business logic and algorithms specific to the target application.
5. Firmware Over-the-Air (FOTA) and Configuration Over-the-Air (COTA): These mechanisms enable remote updates and configuration of the embedded system's firmware and settings, respectively, facilitating maintenance and adaptability throughout the product lifecycle.

The software architecture of an embedded system is carefully crafted to meet the specific requirements of the target application, ensuring efficient resource utilization, real-time performance, and reliable operation.

Conclusion

While the hardware architecture and software architecture of an embedded system varies based on the application and class of the device, the general principles remain the same. Embedded systems, with their focus on reliability, efficiency, and real-time performance, have been the backbone of countless devices and applications, enabling automation and control in various domains.

On the next article, we will explore the architecture of edge AI systems in detail.