Over the past few years, many researchers have tried to develop techniques and technologies that can solve combinatorial optimization problems, which entail identifying an optimal item or solution within a set number of possibilities. Past studies have tackled these problems using annealing-inspired computing accelerators based on a variety of technological tools, including quantum, optical and electronic devices.
Most approaches developed so far, however, have not achieved the processing speeds and energy-efficiencies necessary to solve combinatorial optimization problems on a large scale. This prevents them from being implemented in industrial settings and in other real-world environments.
Researchers at Hewlett Packard Labs (HPL), part of Hewlett Packard Enterprise, have recently developed a new memristor-based annealing system that can solve combinatorial optimization problems rapidly and efficiently. This system, presented in a paper published in Nature Electronics, uses an energy-efficient neuromorphic architecture based on a Hopfield neural network, a type of recurrent neural network first disseminated by John J. Hopfield in 1982 that can be used to implement an auto-associative memory.
"In 2008, our group at HPL found the memristor (i.e., memory resistor), a two-terminal device that could store information in its resistance state even when the power was turned off," Suhas Kumar, one of the researchers who carried out the study, told TechXplore. "In 2017, we hypothesized that noisy memristors could be used to construct a Hopfield network, which could be used to solve NP-hard optimization problems instead of their original purpose of associative memory."
View Full Article
No entries found