Seq2seq Architecture with Memory-Centric Neural Network Processor towards Edge AI Computing

The rapid advancement of generative AI has been driven by the introduction of the Transformer model [1] [2]. However, a significant challenge with such models lies in their high computational requirements. As a result, generative AI predominantly relies on online services. Nevertheless, exploring generative AI capabilities on the edge holds significant potential for enabling novel applications. By employing lightweight models, it becomes feasible to design architectures specifically for edge applications.

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Publication Details

Journal
Journal of Signal Processing
Published
2025-06-30
DOI
https://doi.org/10.2299/jsp.29.83
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.00
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article

Seq2seq Architecture with Memory-Centric Neural Network Processor towards Edge AI Computing

Kunio Nobori, Kota Ando, Tetsuya Asai, Y. Saito et al.
Journal of Signal Processing
Advanced Memory and Neural Computing
article

Seq2seq Architecture with Memory-Centric Neural Network Processor towards Edge AI Computing

Kunio Nobori, Kota Ando, Tetsuya Asai, Y. Saito, Ling Yuanchieh
article en

Abstract

The rapid advancement of generative AI has been driven by the introduction of the Transformer model [1] [2]. However, a significant challenge with such models lies in their high computational requirements. As a result, generative AI predominantly relies on online services. Nevertheless, exploring generative AI capabilities on the edge holds significant potential for enabling novel applications. By employing lightweight models, it becomes feasible to design architectures specifically for edge applications.

Journal of Signal ProcessingVol. 29(4)
Tokyo Electron (Japan) (JP), Hokkaido University (JP)
Affordable and clean energy
Openalex Percentile: Top 87%
Advanced Memory and Neural Computing
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