Noise-Aided Invertible Logic from Coupled Nonlinear Systems

Invertible logic is a powerful new unconventional computing paradigm, providing bidirectional operations between inputs and outputs. It has found applications in important critical problems, such as integer factorization and machine learning. Here we propose a network of interconnected nonlinear systems that serve as our probabilistic bits (p-bits) to implement invertible logic in the presence of a noise floor. In the forward (or directed) mode, the inputs are fixed in our network, yielding outputs in accordance with AND, OR, NAND, and NOR logic functions. In the reverse (inverted) mode the output is clamped in the network, and the input nodes fluctuate among all possible logical input values consistent with the different logic functions. So the system acts as a unique invertible logic circuit by exploiting the probabilistic transitions between the dynamical states of the coupled noisy nonlinear systems. Interestingly, both the directed and the inverted mode are most robust and reliable in an optimal band of moderate noise, reminiscent of stochastic resonance. The concept is verified in proof-of-principle electronic circuit experiments, demon-strating the robustness of the architecture and the potential of this idea to be realized in a wide range of physical situations.

Automated summary

Invertible logic is a powerful unconventional computing paradigm that enables bidirectional operations in the presence of noise. It can be implemented using a network of interconnected nonlinear systems. The system acts as a unique invertible logic circuit by exploiting the probabilistic transitions between the dynamical states of the coupled noisy nonlinear systems.