Optimizing 3D NoC Design for Energy Efficiency: A Machine Learning Approach

Three-dimensional (3D) Network-on-Chip (NoC) is an emerging technology that has the potential to achieve high performance with low power consumption for multicore chips. However, to fully realize their potential, we need to consider novel 3D NoC architectures. In this paper, inspired by the inherent advantages of small-world (SW) 2D NoCs, we explore the design space of SW network-based 3D NoC architectures. We leverage machine learning to intelligently explore the design space to optimize the placement of both planar and vertical communication links for energy efficiency. We demonstrate that the optimized 3D SW NoC designs perform significantly better than their 3D MESH counterparts. On an average, the 3D SW NoC shows 35% energy-delay-product (EDP) improvement over 3D MESH for the nine PARSEC and SPLASH2 benchmarks considered in this work. The highest performance improvement of 43% was achieved for RADIX. Interestingly, even after reducing the number of vertical links by 50%, the optimized 3D SW NoC performs 25% better than the fully connected 3D MESH, which is a strong indication of the effectiveness of our optimization methodology.