A Design Framework for Invertible Logic

Invertible logic using a probabilistic magnetoresistive device model has been recently presented that can compute functions in bidirectional ways and solve several problems quickly, such as factorization and combinational optimization. In this article, we present a design framework for invertible logic circuits. Our approach makes use of linear programming to create a Hamiltonian library with the minimum number of nodes for small invertible-logic functions. In addition, as the device model is approximated based on stochastic computing in synthesizable SystemVerilog, a faster simulation using the compiled SystemC binary is realized than a conventional SPICE-level simulation and is verified using field-programmable gate array (FPGA) as prototyping. Using our design framework, several invertible-logic circuits are designed and emulated (verified) in SystemC, exhibiting five order-of-magnitude faster simulation than conventional work.