A Brain-inspired Hierarchical Interactive In-memory Computing System and its Application in Video Sentiment Analysis

Video sentiment analysis can effectively establish the relationship between the emotion state and the multimodal information, while still suffer from intensive computation and low efficiency, due to the von Neumann computing architecture. Here, we present a brain-inspired hierarchical interactive in...

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Vydáno v:	IEEE transactions on circuits and systems for video technology Ročník 33; číslo 12; s. 1
Hlavní autoři:	Ji, Xiaoyue, Dong, Zhekang, Han, Yifeng, Lai, Chun Sing, Qi, Donglian
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.12.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Brain Brain-inspired Computational efficiency Computer memory Computing time Data mining Feature extraction hierarchical interactive In-memory computing in-memory computing (IMC) Interactive systems Memristors Modules Nanomaterials Robustness (mathematics) Semantics Sentiment analysis Synapses Transformers
ISSN:	1051-8215, 1558-2205
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Abstract	Video sentiment analysis can effectively establish the relationship between the emotion state and the multimodal information, while still suffer from intensive computation and low efficiency, due to the von Neumann computing architecture. Here, we present a brain-inspired hierarchical interactive in-memory computing (IMC) system, which can efficiently solve 'von Neumann bottleneck', enabling cross-modal interactions and semantic gap elimination. First, a 1T1M synapse array is fabricated using cost-effective, highly stable, flexible, and eco-friendly carbon materials, offering efficient analog multiply-accumulate operations. To illustrate the complexity of the proposed brain-inspired hierarchical interactive IMC system, three modules are proposed: 1) unimodal extraction module, 2) hierarchical interactive module, 3) output module. Furthermore, the proposed system is validated by applying it to video sentiment analysis. The experimental results demonstrate that the proposed system outperforms the existing state-of-the-art methods with high computational efficiency and good robustness. This work opens up a new way to achieve the deep integration of nanomaterials, deep learning, and modern electronics into IMC.
AbstractList	Video sentiment analysis can effectively establish the relationship between the emotion state and the multimodal information, while still suffer from intensive computation and low efficiency, due to the von Neumann computing architecture. Here, we present a brain-inspired hierarchical interactive in-memory computing (IMC) system, which can efficiently solve ‘von Neumann bottleneck’, enabling cross-modal interactions and semantic gap elimination. First, a 1T1M synapse array is fabricated using cost-effective, highly stable, flexible, and eco-friendly carbon materials, offering efficient analog multiply-accumulate operations. To illustrate the complexity of the proposed brain-inspired hierarchical interactive IMC system, three modules are proposed: 1) unimodal extraction module, 2) hierarchical interactive module, 3) output module. Furthermore, the proposed system is validated by applying it to video sentiment analysis. The experimental results demonstrate that the proposed system outperforms the existing state-of-the-art methods with high computational efficiency and good robustness. This work opens up a new way to achieve the deep integration of nanomaterials, deep learning, and modern electronics into IMC.
Author	Lai, Chun Sing Qi, Donglian Dong, Zhekang Han, Yifeng Ji, Xiaoyue
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SubjectTerms	Brain Brain-inspired Computational efficiency Computer memory Computing time Data mining Feature extraction hierarchical interactive In-memory computing in-memory computing (IMC) Interactive systems Memristors Modules Nanomaterials Robustness (mathematics) Semantics Sentiment analysis Synapses Transformers
Title	A Brain-inspired Hierarchical Interactive In-memory Computing System and its Application in Video Sentiment Analysis
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