Embedded Neural Network

Embedded neural networks focus on designing and optimizing neural network models for deployment on resource-constrained devices. Current research emphasizes efficient architectures like spiking neural networks and state-space models, along with novel training algorithms such as single-pass learning methods, to minimize computational cost and memory footprint while maintaining accuracy. This field is crucial for advancing edge AI applications, enabling powerful machine learning capabilities in devices with limited power and processing capabilities, and also raises important security concerns regarding model extraction and robustness to physical attacks.