BrickNN: Towards Brick-like Self-Organizing Modular Neural Networks for Plastic and Collective AIoT Learning

Large-scale neural models face significant deployment challenges in resource-constrained AIoT environments that demand computational efficiency and collaborative adaptability. Modular Neural Networks (MNNs) offer promising solutions but struggle with automatic construction of functionally differentiated modules and lack efficient decentralized collaboration mechanisms for distributed AIoT deployments. To address these challenges, we propose BrickNN, a self-organizing modular neural network framework for distributed AIoT systems. BrickNN consists of three key components. First, a constraint-guided module construction mechanism enables functional differentiation through Specialization-Biased Module Design, Routing-Induced Specialization Learning, and Partitioned Static Signature Initialization. Second, Input-Driven Hierarchical Routing generates routing decisions from input features and decouples semantic module selection from execution constraints, enabling efficient and adaptive routing under heterogeneous deployment conditions. Third, Task-aware Decentralized Collaboration performs reliability-aware module collaboration and selective aggregation, enabling efficient knowledge sharing without central coordination. Extensive experiments on visual recognition and IoT sensing benchmarks demonstrate that BrickNN consistently improves system efficiency, achieving up to 10.38%–497.27% gains over baseline methods under varying communication conditions. The code is available at https://github.com/limmodular/BrickNN .