Xplace
Lixin Liu, Bangqi Fu, Martin D. F. Wong, Evangeline F. Y. Young
Abstract
Placement serves as a fundamental step in VLSI physical design. Recently, GPU-based global placer DREAMPlace[1] demonstrated its superiority over CPU-based global placers. In this work, we develop an extremely fast GPU accelerated global placer Xplace which achieves around 2x speedup with better solution quality compared to DREAMPlace. We also plug a novel Fourier neural network into Xplace as an extension to further improve the solution quality. We believe this work not only proposes a new, fast, extensible placement framework but also illustrates a possibility to incorporate a neural network component into a GPU accelerated analytical placer.
Topics & Concepts
Computer sciencePlacer miningSpeedupVery-large-scale integrationComputational scienceParallel computingArtificial neural networkComputer engineeringComputer architectureArtificial intelligenceEmbedded systemMaterials scienceMetallurgyVLSI and FPGA Design TechniquesVLSI and Analog Circuit TestingAdvancements in Photolithography Techniques