Integration of net-length factor with timing- and routability-riven clustering algorithms

Hanyu Liu, Senthilkumar T. Rajavel, Ali Akoglu

Research output: Contribution to journalArticlepeer-review


In FPGA CAD flow, the clustering stage builds the foundation for placement and routing stages and affects performance parameters, such as routability, delay, and channel width significantly. Net sharing and criticality are the two most commonly used factors in clustering cost functions. With this study, we first derive third term net-length factor and then design a generic method for integrating net length into the clustering algorithms. Net-length factor enables characterizing the nets based on the routing stress they might cause during later stages of the CAD flow and is essential for enhancing the routability of the design. We evaluate the effectiveness of integrating net length as a factor into the well-known timing (T-VPack)- depopulation (T-NDPack)- and routability (iRAC and T-RPack)-driven clustering algorithms. Through exhaustive experimental studies we show that net-length factor consistently helps improve the channel-width performance of routability- depopulation- and timing-driven clustering algorithms that do not explicitly target low fan-out nets in their cost functions. Particularly net-length factor leads to average reduction in channel width for T-VPack T-RPack and T-NDPack by 11.6% 10.8% and 14.2% respectively and in a majority of the cases improves the critical-path delay without increasing the array size. Categories and Subject Descriptors: B.7.1 [Integrated Circuits]: Types and Design Styles-Gate arrays General Terms: Design Performance.

Original languageEnglish (US)
Article number12
JournalACM Transactions on Reconfigurable Technology and Systems
Issue number3
StatePublished - Oct 2013


  • Channel width
  • Clustering
  • Field programmable gate array
  • Net length prediction

ASJC Scopus subject areas

  • General Computer Science


Dive into the research topics of 'Integration of net-length factor with timing- and routability-riven clustering algorithms'. Together they form a unique fingerprint.

Cite this