Solving Max‐Cut Problem Using Spiking Boltzmann Machine Based on Neuromorphic Hardware with Phase Change Memory

Solving Max‐Cut Problem Using Spiking Boltzmann Machine Based on Neuromorphic Hardware with Phase Change Memory

Abstract Efficiently solving combinatorial optimization problems (COPs) such as Max‐Cut is challenging because the resources required increase exponentially with the problem size. This study proposes a hardware‐friendly method for solving the Max‐Cut problem by implementing a spiking neural network...

Full description

Saved in:
Bibliographic Details
Main Authors: Yu Gyeong Kang, Masatoshi Ishii, Jaeweon Park, Uicheol Shin, Suyeon Jang, Seongwon Yoon, Mingi Kim, Atsuya Okazaki, Megumi Ito, Akiyo Nomura, Kohji Hosokawa, Matthew BrightSky, Sangbum Kim
Format: Article
Language:English
Published: Wiley 2024-12-01
Series:Advanced Science
Subjects:
Boltzmann machines
combinatorial optimizations
leaky integrate‐and‐fire neurons
max‐cut problems
neuromorphic hardware
spiking neural networks
Online Access:https://doi.org/10.1002/advs.202406433
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://doi.org/10.1002/advs.202406433

Similar Items