Dynamic machine vision with retinomorphic photomemristor-reservoir computing

Hongwei Tan*, Sebastiaan van Dijken*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

27 Citations (Scopus)
52 Downloads (Pure)

Abstract

Dynamic machine vision requires recognizing the past and predicting the future of a moving object based on present vision. Current machine vision systems accomplish this by processing numerous image frames or using complex algorithms. Here, we report motion recognition and prediction in recurrent photomemristor networks. In our system, a retinomorphic photomemristor array, working as dynamic vision reservoir, embeds past motion frames as hidden states into the present frame through inherent dynamic memory. The informative present frame facilitates accurate recognition of past and prediction of future motions with machine learning algorithms. This in-sensor motion processing capability eliminates redundant data flows and promotes real-time perception of moving objects for dynamic machine vision.

Original languageEnglish
Article number2169
Pages (from-to)1-9
Number of pages9
JournalNature Communications
Volume14
Issue number1
DOIs
Publication statusPublished - Dec 2023
MoE publication typeA1 Journal article-refereed

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