Optimized training and scalable implementation of Conditional Deep Neural Networks with early exits for Fog-supported IoT applications
The incoming IoT big data era requires efficient and resource-constrained mining of large sets of distributed data. This paper explores a possible approach to this end, combining the two emerging paradigms of Conditional Neural Networks with early exits and Fog Computing. Apart from describing the general framework, we provide four specific contributions.