ANN Based Admission Control for On-Chip Networks

We propose an admission control method in Network-on-Chip (NoC) with a centralized Artificial Neural Network (ANN) admission controller, which can improve system performance by predicting the most appropriate injection rate of each node via the network performance information. In the online control process, a data preprocessing unit is applied to simplify the ANN architecture and make the prediction results more accurate. Based on the preprocessed information, the ANN predictor determines the control strategy and broadcasts it to each node where the admission control will be applied. Compared to the previous work, our method builds up a high-fidelity model between the network status and the injection rate regulation. The full-system simulation results show that our proposed method can enhance application performance by 17.8% on average and up to 23.8%.