3D-STAF: scalable temperature and leakage aware floorplanning for three-dimensional integrated circuits

  • Authors:
  • Pingqiang Zhou;Yuchun Ma;Zhouyuan Li;Robert P. Dick;Li Shang;Hai Zhou;Xianlong Hong;Qiang Zhou

  • Affiliations:
  • Tsinghua University, Beijing, China;Tsinghua University, Beijing, China;Synopsys, Inc., Beijing, China;Northwestern University, Evanston, IL;Queen's University, Kingston, ON, Canada;Northwestern University, Evanston, IL;Tsinghua University, Beijing, China;Tsinghua University, Beijing, China

  • Venue:
  • Proceedings of the 2007 IEEE/ACM international conference on Computer-aided design
  • Year:
  • 2007

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Abstract

Thermal issues are a primary concern in the three-dimensional (3D) integrated circuit (IC) design. Temperature, area, and wire length must be simultaneously optimized during 3D floorplanning, significantly increasing optimization complexity. Most existing floorplanners use combinatorial stochastic optimization techniques, hampering performance and scalability when used for 3D floorplanning. In this work, we propose and evaluate a scalable, temperature-aware, force-directed floorplanner called 3D-STAF. Force-directed techniques, although efficient at reacting to physical information such as temperature gradients, must eventually eliminate overlap. This can cause significant displacement when used for heterogeneous blocks. To smooth the transition from an unconstrained 3D placement to a legalized, layer-assigned floorplan, we propose a three-stage force-directed optimization flow combined with new legalization techniques that eliminate white spaces and block overlapping during multi-layer floorplanning. A temperature-dependent leakage model is used within 3D-STAF to permit optimization based on the feedback loop connecting thermal profile and leakage power consumption. 3D-STAF has good performance that scales well for large problem instances. Compared to recently published 3D floorplanning work, 3D-STAF improves the area by 6%, wire length by 16%, via count by 22%, peak temperature by 6% while running nearly 4x faster on average.