Intelligent NoC with neuro-fuzzy bandwidth regulation for a 51 IP object recognition processor

Balancing the execution times of concurrent tasks in a multi-core processor is critical to achieving good performance scaling with increasing core count. However, this is difficult when the tasks´ execution times are not known in advance. In this work, we propose an intelligent Network-on-Chip that performs bandwidth regulation using weighted round robin packet arbitration to balance the execution times of 4 Feature Extraction Clusters whose workloads vary depending on the input content. A neuro-fuzzy inference block, named the Intelligent Inference Engine, predicts the workload of each FEC, and assigns a priority weight to each FEC channel. As a result, 34% reduction in synchronization overhead due to unbalanced execution time was achieved, and the overall execution time was reduced by 11.5%.