This presentation will present a state-of-the art, Multi-Level Graph clusterer, partitioner called AMASS. AMASS may be used for Machine Learning (ML) and VLSI circuit design. For ML applications, AMASS may be used to partition arbitrary sized graphs to a fixed number of partitions. The key benefit here is scalable AI, where the original graph may be scaled down and fed to a fixed size NN. For VLSI design, the partitioned may be used as a divide and conquer methodology for block-based design of large ICs. We contrast AMASS against four other popular Partitioning tools, including hMetis, Patoh, Kahypar and Kahypar_MT. It is experimentally demonstrated, on a total of 43, conference benchmarks that AMASS manages to achieve superior QOR in terms of cutsize, area ratio and longest graph path fragmentation over other approaches.
