User Cooperation Network Coding Approach for NoC Performance Improvement

The astonishing rate of sensing modalities and data generation poses a tremendous impact on computing platforms for providing real-time mining and prediction capabilities. We are capable of monitoring thousands of genes and their interactions, but we lack efficient computing platforms for large-scale (exa-scale) data processing. Towards this end, we propose a novel hierarchical Network-on-Chip (NoC) architecture that exploits user-cooperated network coding (NC) concepts for improving system throughput. Our proposed architecture relies on a light-weighted subnet of cooperation unit routers (CUR) for multicast traffic. Coding network interface (CNI) performs encoding/decoding of NC symbols and shares the data flows among cooperation units(CUs). We endow our proposed NC-based NoC architecture with: (i) a corridor routing algorithm (CRA) for maximizing network throughput and (ii) an adaptive flit dropping (AFD) scheme to mitigate congestion, branch-blocking and deadlock at run-time. The experimental results demonstrate that our proposed platform offers up to 127X multicast throughput improvement over multiple-unicast and XY tree-based multicast under synthetic collective traffic scenario. We have evaluated the proposed platform with different realworld benchmarks under network sizes of 4x4 to 32x32. Simulation results show 21%--91% latency improvement and up to 25X runtime reduction over conventional mesh NoC performing genetic-algorithm based protein folding analysis. FPGA implementation results show minimal overhead.