BORE: Energy-Efficient Banded Vector Similarity Search with Optimized Range Encoding for Memory-Augmented Neural Network-Reference-Cited by-同舟云学术

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BORE: Energy-Efficient Banded Vector Similarity Search with Optimized Range Encoding for Memory-Augmented Neural Network

Published:2024-03-25 Issue: Volume:12 Page:1-6
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Container-title:2024 Design, Automation & Test in Europe Conference & Exhibition (DATE)
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Author:

Huang Chi-Tse¹,Chang Cheng-Yang¹,Cheng Hsiang-Yun²,Wu An-Yeu¹

Affiliation:

1. Graduate Institute of Electronics Engineering, National Taiwan University,Taipei,Taiwan

2. Research Center for Information Technology Innovation, Academia Sinica,Taipei,Taiwan

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10546498/10546499/10546659.pdf?arnumber=10546659

Reference21 articles.

1. Meta-learning with memory-augmented neural networks;Adam,2016

2. Matching networks for one shot learning;Oriol;Ad-vances in neural information processing systems,2016

3. X-mann: A crossbar based architecture for memory augmented neural networks;Ashish,2019

4. ICE: An Intelligent Cognition Engine with 3D NAND-based In-Memory Computing for Vector Similarity Search Accel-eration;Han-Wen,2022

5. One-shot learning with memory-augmented neural networks using a 64-kbit, 118 GOPS/W RRAM-based non-volatile asso-ciative memory;Haitong,2021

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