Low-Power Sparse Hyperdimensional Encoder for Language Recognition

Mohsen Imani; John Hwang; Tajana Rosing; Abbas Rahimi; Jan M. Rabaey

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Low-Power Sparse Hyperdimensional Encoder for Language Recognition

机译：用于语言识别的低功率稀疏超维编码器

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摘要

Online learning for data analysis, categorization and anomaly detection has become a key technique in a range of adaptive embedded applications. In this article the authors propose a very low power encoder design using a sparse, hyperdimensional representation of letters and words in natural language and they show that this representation and their design can be used with high energy efficiency for a language recognition task. —Axel Jantsch, Royal Institute of Technology Electronics, Computer and Software Systems

机译：用于数据分析，分类和异常检测的在线学习已成为一系列自适应嵌入式应用程序中的一项关键技术。在本文中，作者提出了一种使用自然语言中的字母和单词的稀疏，超维表示的低功耗编码器设计，并且他们表明，这种表示及其设计可以高效地用于语言识别任务。 — 皇家技术电子，计算机和软件系统研究所的Axel Jantch

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来源
《Design & Test of Computers, IEEE》 |2017年第6期|94-101|共8页
作者
Mohsen Imani; John Hwang; Tajana Rosing; Abbas Rahimi; Jan M. Rabaey;
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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种 eng
中图分类
关键词
High definition video; Encoding; Switches; Associative memory; Computers; System-on-chip; Power demand; Electronic learning;

机译：高清晰度视频;编码;开关;关联存储器;计算机;片上系统;功率需求;电子学习;

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Low-Power Sparse Hyperdimensional Encoder for Language Recognition

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