To enable emerging applications such as deep machine learning and graph processing, 3D network-on-chip (NoC) enabled heterogeneous manycore platforms that can integrate many processing elements (PEs) are needed. However, designing such complex systems with multiple objectives can be challenging due to the huge associated design space and long evaluation times. To optimize such systems, we propose a new multi-objective design space exploration framework called MOELA that combines the benefits of evolutionary-based search with a learning-based local search to quickly determine PE and communication link placement to optimize multiple objectives (e.g., latency, throughput, and energy) in 3D NoC enabled heterogeneous manycore systems. Compared to state-of-the-art approaches, MOELA increases the speed of finding solutions by up to 128x, leads to a better Pareto Hypervolume (PHV) by up to 12.14x and improves energy-delay-product (EDP) by up to 7.7% in a 5-objective scenario.

翻译：为支持深度机器学习与图处理等新兴应用，需要构建由三维片上网络（NoC）赋能、能够集成大量处理单元（PE）的异构多核平台。然而，由于设计空间极其庞大且评估时间较长，针对多目标优化设计此类复杂系统极具挑战性。为此，我们提出一种名为MOELA的新型多目标设计空间探索框架，该框架融合了基于进化搜索与基于学习局部搜索的优势，能够快速确定三维NoC异构多核系统中处理单元与通信链路的布局方案，从而优化多个目标（如延迟、吞吐量和能耗）。与现有方法相比，MOELA在五目标场景下将求解速度提升高达128倍，获得的帕累托超体积（PHV）指标提升至12.14倍，并将能量延迟积（EDP）改善达7.7%。