Fine-Grained Bandwidth Adaptivity in Networks-on-Chip Using Bidirectional Channels

Abstract

Networks-on-Chip (NoC) serve as efficient and scalable communication substrates for many-core architectures. Currently, the bandwidth provided in NoCs is over provisioned for their typical usage case. In real-world multi-core applications, less than 5% of channels are utilized on average. Large bandwidth resources serve to keep network latency low during periods of peak communication demands. Increasing the average channel utilization through narrower channels could improve the efficiency of NoCs in terms of area and power, however, in current NoC architectures this degrades overall system performance. Based on thorough analysis of the dynamic behaviour of real workloads, we design a novel NoC architecture that adapts to changing application demands. Our architecture uses fine-grained bandwidth-adaptive bidirectional channels to improve channel utilization without negatively affecting network latency. Running PARSEC benchmarks on a cycle-accurate full-system simulator, we show that fine-grained bandwidth adaptivity can save up to 75% of channel resources while achieving 92% of overall system performance compared to the baseline network, no performance is sacrificed in our network design configured with 50% of the channel resources used in the baseline.