Towards a Modular Paradigm: Developing and Deploying Artificial Intelligence in Embedded Software Systems

Abstract

The rapid advancement of artificial intelligence (AI) has ushered in a new era of innovation for embedded systems, leading to the widespread deployment of embedded AI software across diverse domains such as smart homes and industrial automation. This proliferation, however, concurrently imposes stringent demands on software flexibility to accommodate evolving functionalities, heterogeneous hardware, and dynamic operational environments. In this context, modular design has emerged as a pivotal architectural paradigm. Modular embedded AI software effectively addresses these challenges by decomposing complex, monolithic systems into a cohesive set of independent, self-contained, and highly cohesive modules. This decomposition significantly enhances development efficiency by enabling parallel development, simplifying debugging, and facilitating component reuse. Furthermore, it substantially improves system maintainability, allowing for targeted updates, bug fixes, or algorithm replacements within specific modules without necessitating a full system overhaul. This inherent adaptability empowers the software to respond more effectively to rapidly changing market demands and technological upgrades. Crucially, a well-defined modular architecture provides a standardized framework for the efficient integration and deployment of diverse AI algorithms, allowing developers to plug in, test, and compare different models for perception, decision-making, or control with minimal friction. This paper elaborates on the core principles, architectural patterns, and implementation methodologies for constructing modular embedded AI systems. It argues that the strategic adoption of modularity is not merely a software engineering best practice but a critical enabler for building robust, scalable, and future-proof intelligent embedded systems capable of sustaining the next wave of innovation at the intersection of AI and edge computing.