A Scalable Mixed Synthesis Framework for Heterogeneous Networks
Max Austin1, Scott Temple1, Walter Lau Neto1, Luca Amarù2, Xifan Tang1 and Pierre-Emmanuel Gaillardon1
1LNIS, University of Utah, Salt Lake City, Utah, USA
2Synopsys Inc., Design Group, Sunnyvale, California, USA
ABSTRACT
We present a new logic synthesis framework which produces efficient post-technology mapped results on heterogeneous networks containing a mix of different types of logic. This framework accomplishes this by breaking down the circuit into sections using a hypergraph k-way partitioner and then determines the best-fit logic representation for each partition between two Boolean networks, And-Inverter Graphs (AIG) and Majority-Inverter Graphs (MIG), which have been shown to perform better over each other on different types of logic. Experimental results show that over a set of Open Piton Design Benchmarks (OPDB) and OpenCores benchmarks, our proposed methodology outperforms state-of-the-art academic tools in Area- Delay Product (ADP), Power-Delay Product (PDP), and Energy- Delay Product (EDP) by 5%, 2%, and 15% respectively after performing Application Specific Integrated Circuits (ASIC) technology mapping as well as showing a 54% improvement in runtime over conventional MIG optimization.
