Demonstration of Neuromodulation‐inspired Stashing System for Energy‐efficient Learning of Spiking Neural Network using a Self‐Rectifying Memristor Array (Adv. Funct. Mater. 29/2022). (18th July 2022)

• Record Type: Journal Article
• Authors: Cheong, Woon Hyung; Jeon, Jae Bum; In, Jae Hyun; Kim, Geunyoung; Song, Hanchan; An, Janho; Park, Juseong; Kim, Young Seok; Hwang, Cheol Seong; Kim, Kyung Min
• Is Part Of: Advanced functional materials. Volume 32:Number 29(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-07-18
• Subjects: memristors -- neural network -- neuromodulation -- neuromorphic devices -- stashing system; Materials -- Periodicals; Chemical vapor deposition -- Periodicals; 620.11
• DOI: 10.1002/adfm.202270169
• Languages: English
• ISSNs: 1616-301X

Abstract

Abstract : Neuromodulation In article number 2200337, Cheol Seong Hwang, Kyung Min Kim, and co‐workers demonstrate a stashing system that can increase the energy efficiency of a conventional spiking neural network, available on a chip that performs artificial intelligence. This system comes from brain neuromodulation, which is a technology that can efficiently handle mathematical operations for artificial intelligence by imitating the continuous changes in the topology of the neural network according to the situation.