Optimizing Deep Learning Architectures forEnhanced Computational Efficiency

In alignment with the mission of Frontiers in Computer Science to advance both fundamental and applied computationalsciences, this study addresses the pressing need for computationally efficient and interpretable deep learning architectures.Traditional deep neural networks often suffer from static structures, leading to inefficiencies in computation and challengesin interpretability, particularly when applied across diverse domains. To overcome these limitations, we introduce a novelframework that synergizes a Dynamic Compositional Architecture (DCA) with a Knowledge-Embedded Adaptive Strategy(KEAS). The DCA reimagines neural network design by structuring the model as a directed acyclic graph, where each noderepresents a functional module activated conditionally based on input characteristics. This dynamic activation facilitates efficientrouting of computations, enabling the model to adapt its depth and breadth in real-time. Complementing this, KEAS integratesdomain-specific knowledge through symbolic priors and adaptive modulation, guiding the learning process to favor semanticallymeaningful pathways and enhancing both robustness and interpretability. Empirical demonstrate that our integrated approachnot only reduces computational overhead but also maintains or improves predictive performance across various tasks. Thiswork contributes to the fields of software engineering and theoretical computer science by providing a scalable, interpretable,and efficient deep learning paradigm, resonating with the journal’s emphasis on innovative computational methodologies.