Neural Architecture refers to the structural design and configuration of artificial neural networks, including the number and arrangement of layers, node connectivity patterns, activation functions, and learning algorithms that determine how these systems process information and learn from data.

For technical leaders, Neural Architecture represents a critical design domain that significantly impacts AI system capabilities, performance, and resource requirements. Modern neural architectures have evolved from simple feed-forward networks to sophisticated structures including convolutional networks (CNNs) optimized for visual data, recurrent and transformer architectures for sequential information, graph neural networks for relationship-based data, and generative adversarial networks for creating synthetic content. The design space encompasses multiple dimensions: depth (number of layers); width (neurons per layer); connectivity patterns (dense, sparse, residual connections); parameter sharing approaches; and optimization strategies. Enterprise architects implementing neural systems must navigate complex trade-offs between model performance, interpretability, computational efficiency, and training data requirements. Neural Architecture Search (NAS) and AutoML approaches are increasingly employed to algorithmically discover optimal architectures for specific tasks, though these require significant computational resources. For CIOs and CTOs, neural architecture decisions have far-reaching implications for infrastructure requirements, deployment options, and operational considerations, particularly as models scale to billions of parameters. Organizations typically require multilayered architectural approaches incorporating model architecture design, distributed training frameworks, deployment pipelines, and monitoring systems that together enable effective AI operations. As these systems increasingly influence critical business processes, architects must also address considerations around explainability, bias mitigation, and governance frameworks that ensure neural systems operate within appropriate bounds regardless of their architectural complexity.