Combinatorial optimization problems are prevalent in various fields, but obtaining exact
solutions remains challenging due to the combinatorial explosion with increasing problem …

We describe a hybrid analog-digital computing approach to solve important combinatorial
optimization problems that leverages memristors (two-terminal nonvolatile memories). While …

We demonstrate an analog neuro-optimization hardware, suitable for solving a large
number of combinatorial optimization problems, based on a crossbar circuit with 4096 …

To tackle important combinatorial optimization problems, a variety of annealing-inspired
computing accelerators, based on several different technology platforms, have been …

Memristors have recently received significant attention as device-level components for
building a novel generation of computing systems. These devices have many promising …

Satisfying the rapid evolution of artificial intelligence (AI) algorithms requires exponential
growth in computing resources, which, in turn, presents huge challenges for deploying AI …

The identification of the memristor as fourth fundamental circuit element and, eventually, its
fabrication in the HP labs provide new capabilities for in-memory computing. While there …

Ising solvers offer a promising physics-based approach to tackle the challenging class of
combinatorial optimization problems. However, typical solvers operate in a quadratic energy …

Crossbars of nanoscale memristors are being fabricated to serve as high-density non-
volatile memory devices. The flow of current through memristor crossbars has been recently …

As an NP-hard problem, feature selection often utilizes nature-inspired methods, like the
genetic algorithm (GA), to find the partly optimal solution but still suffers from the …