2023/03/10 by Sirui Qi, Qi, Sirui, Yingheng Li +5
Computer Science · Engineering · Materials Science · #3D IC and TSV technologies #FOS: Computer and information sciences #Hardware Architecture (cs.AR) #Interconnection Networks and Systems #Machine Learning (cs.LG) #Phase-change materials and chalcogenides
paper · pdf · doi:10.48550/arxiv.2303.06169
openalex publication_date 2023/03/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.