AIG Rewriting Using 5-Input Cuts

Abstract: Rewriting is a common approach to logic optimization based on local transformations. Most commercially available logic synthesis tools include a rewriting engine that may be used multiple times on the same netlist during optimization. This thesis presents an And-Inverter graph based rewriting algorithm using 5-input cuts. The best circuits are pre-computed for a subset of NPN classes of 5-variable functions. Cut enumeration and Boolean matching are used to identify replacement candidates. The presented approach is expected to complement existing rewriting approaches which are usually based on 4-input cuts. The experimental results show that, by adding the new rewriting algorithm to ABC synthesis tool, we can further reduce the area of heavily optimized large circuits by 5.57% on average.