Utilizing XMG-based Synthesis to Preserve Self-Duality for RFET-Based Circuits

—Individual transistors based on emerging reconﬁg- urable nanotechnologies exhibit electrical conduction for both types of charge carriers. These transistors (referred to as Recon- ﬁgurable Field-Effect Transistors (RFETs)) enable dynamic reconﬁguration to demonstrate either a p- or an n-type functionality. This duality of functionality at the transistor level is efﬁciently abstracted as a self-dual Boolean logic, that can be physically re- alized with fewer RFET transistors compared to the contemporary CMOS technology. Consequently, to achieve better area reduction for RFET-based circuits, the self-duality of a given circuit should be preserved during logic optimization and technology mapping. In this paper, we speciﬁcally aim to preserve self-duality by using Xor-Majority Graphs (XMGs) as the logic representation during logic synthesis and technology mapping. We propose a synthesis ﬂow that uses new restructuring techniques, called rewriting and resubstitution for XMGs to preserve self-duality during technology-independent logic synthesis. For technology mapping, we use a novel open-source and a logic-representation agnostic mapping tool. Using the above-proposed XMG-based ﬂow, we demonstrate its beneﬁts by comparing post-mapping area for synthetic and cryptographic benchmarks with three different synthesis ﬂows: (i) AIG-based optimization and AIG-based mapping; (ii) XMG-based optimization with AIG-based mapping; (iii) AIG-based optimization with logic-representation agnostic mapping. Our experiments show that the proposed XMG- based ﬂow efﬁciently preserves self-duality and achieves the best area results for RFET-based circuits (up to 12 . 36% area reduction) with respect to the baseline.