A Configurable Hardware Architecture for Runtime Application of Network Calculus

Abstract

Network Calculus has been a foundational theory for analyzing and ensuring Quality-of-Service (QoS) in a variety of networks including Networks on Chip (NoCs). To fulfill dynamic QoS requirements of applications, runtime application of network calculus is essential. However, the primitive operations in network calculus such as arrival curve, min-plus convolution and min-plus deconvolution are very time consuming when calculated in software because of the large volume and long latency of computation. For the first time, we propose a configurable hardware architecture to enable runtime application of network calculus. It employs a unified pipeline that can be dynamically configured to efficiently calculate the arrival curve, min-plus convolution, and min-plus deconvolution at runtime. We have implemented and synthesized this hardware architecture on a Xilinx FPGA platform to quantify its performance and resource consumption. Furthermore, we have built a prototype NoC system incorporating this hardware for dynamic flow regulation to effectively achieve QoS at runtime.