S
S. Liu
Researcher at University of Southern California
Publications - 5
Citations - 51
S. Liu is an academic researcher from University of Southern California. The author has contributed to research in topics: Graph (abstract data type) & Finite-state machine. The author has an hindex of 4, co-authored 5 publications receiving 51 citations.
Papers
More filters
Proceedings ArticleDOI
Alleviating routing congestion by combining logic resynthesis and linear placement
TL;DR: In this approach, the logic is restructured using an intermediate placement solution and then the placement is adjusted to match the new logic structure to obtain channel density reductions that are not possible by physical design operations such as lateral shifting, pin permutation, and channel routing.
Proceedings ArticleDOI
Application-driven design automation for microprocessor design
Chi-Ying Tsui,H.-T. Chen,G. Cheng,S. Liu,S. Wu,Alvin M. Despain,I. Pyo,C. L. Su,Ing-Jer Huang,K.-R. Pan,Y.-S. Koh +10 more
TL;DR: An overview of the application-driven design automation system (ADAS) for microprocessor design, which spans language design, compiler design, instruction set design, microarchitecture, and VLSI implementation is presented.
Proceedings ArticleDOI
A Fast State Assignment Procedure for Large FSMs
TL;DR: A new method to solve the graph embedding problem which is the main step in the state assignment process is presented, to place the state adjacency graph in a two-dimensional grid while minimizing the total wire length.
Proceedings ArticleDOI
PLATO P: PLA timing optimization by partitioning
TL;DR: This paper addresses the problem of partitioning a large PLA into a number of smaller PLA's (sub-PLA's) such that the total area of these sub- PLA's is minimum and the cycle time of the partitioned circuit is minimized.
Journal ArticleDOI
State assignment based on two-dimensional placement and hypercube mapping
TL;DR: A new scheme to solve the graph embedding problem which is the main step in the state assignment process is presented, which places the graph in a two-dimensional array while minimizing the total edge length, and then maps this two- dimensional array into an n-dimensional hypercube with dilation of at most 2.