The Paradigm of Collective Computing

14th October 2020

Timing : 1 pm EST

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Many computationally hard problems, for example combinatorial optimization, can be mapped into the problem of finding the ground-state of an Ising Hamiltonian. Here, we present a continuous-time dynamical system (CTDS) approach where the ground-state solution appears as stable points or attractor states of the CTDS. In particular, we harness the emergent dynamics of a coupled network of electronic phase-transition nano-oscillators (PTNOs) to build an Ising Hamiltonian solver. The hardware fabric comprises capacitively coupled network of injection-locked stochastic PTNOs with bi-stable phases emulating artificial Ising spins. The experimental prototype can solve a benchmark non-deterministic polynomial time (NP)-hard graph partitioning problem called MaxCUT with high probability of success. Using experimentally calibrated numerical simulations, we investigate the scalability of the hardware. We find over five orders of magnitude improvement in energy efficiency when compared with fully digital, quantum and optical approaches. Such an energy efficient CTDS hardware will significantly benefit industrial planning and manufacturing, defense and cyber-security, bioinformatics and drug discovery, thereby ushering in a new era of collective computing.