A new approach to effective circuit clustering
ICCAD '92 1992 IEEE/ACM international conference proceedings on Computer-aided design
Spectral K-way ratio-cut partitioning and clustering
DAC '93 Proceedings of the 30th international Design Automation Conference
Recent directions in netlist partitioning: a survey
Integration, the VLSI Journal
A general framework for vertex orderings with applications to circuit clustering
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Metrics for structural logic synthesis
Proceedings of the 2002 IEEE/ACM international conference on Computer-aided design
A semi-persistent clustering technique for VLSI circuit placement
Proceedings of the 2005 international symposium on Physical design
NTUplace: a ratio partitioning based placement algorithm for large-scale mixed-size designs
Proceedings of the 2005 international symposium on Physical design
mPL6: enhanced multilevel mixed-size placement
Proceedings of the 2006 international symposium on Physical design
Edge separability-based circuit clustering with application to multilevel circuit partitioning
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Proceedings of the 49th Annual Design Automation Conference
Ripple 2.0: high quality routability-driven placement via global router integration
Proceedings of the 50th Annual Design Automation Conference
Analyzing System-Level Information’s Correlation to FPGA Placement
ACM Transactions on Reconfigurable Technology and Systems (TRETS)
| Hi-index | 0.00 |
This work proposes a new problem of identifying large and tangled logic structures in a synthesized netlist. Large groups of cells that are highly interconnected to each other can often create potential routing hotspots that require special placement constraints. They can also indicate problematic clumps of logic that either require resynthesis to reduce wiring demand or specialized datapath placement. At a glance, this formulation appears similar to conventional circuit clustering, but there are two important distinctions. First, we are interested in finding large groups of cells that represent entire logic structures like adders and decoders, as opposed to clusters with only a handful of cells. Second, we seek to pull out only the structures of interest, instead of assigning every cell to a cluster to reduce problem complexity. This work proposes new metrics for detecting structures based on Rent's rule that, unlike traditional cluster metrics, are able to fairly differentiate between large and small groups of cells. Next, we demonstrate how these metrics can be applied to identify structures in a netlist. Finally, our experiments demonstrate the ability to predict and alleviate routing hotspots on a real industry design using our metrics and method.