Communicating Neural Network architectures for resource constrained systems - Abudu, P (2022)
The deployment of millions of embedded sensors plagued by resource constraints in sophisticated, complex and dynamic Internet of Things (IoT) environments continues to inspire the need to build novel architectures and models for automated, efficient inference and communication. In such settings, practical challenges related to energy efficiency, computational power and reliability, tedious design implementation, effective communication, optimal sampling and accurate event classification, prediction and detection exist. Sensors operating in such environments must be capable of overcoming such challenges and enable scalable monitoring of dynamic phenomena whilst respecting limitations computational capabilities and battery energy. We continue to see a shift from reliance on cloud-centric to edge-centric architectures for data processing, inference and actuation. Distributed edge inference techniques address real-time, connectivity, network bandwidth and latency challenges in spatially dis- tributed IoT applications. Achieving efficient, resource-aware communication in such systems is a longstanding challenge. Many current approaches require complex, hand-engineered communication protocols.