.png){kind=icon}
返回
Memristor-based in-memory processor for high precision semantic text classification
.png){kind=icon}
PRE
AI
DOI：10.1016/j.compeleceng.2021.107160.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Text classification is an important component of digital media such as natural language processing, image labeling, sentiment analysis, spam filtering, chatbots, and translators. In this work, effort was devoted to develop an in-memory processor for Bayesian text classification using memristive crossbar architecture, in which memristive switches were employed to store information required for the classification of text. The efficacy of the proposed circuit was tested on two distinct datasets consisting of a total of 55,575 texts. The circuit was found to be efficient to categorize the texts with an average accuracy of 91%. This work paves the way for hardware realization of cognitive systems using in-memory processors.
Keyword:
Memristor
Bayesian classifier
Semantic text classification
In-memory computing
期刊
C
IF:
4.9
论文数:
6.7K
被引数:
1.3W
机构
引用论文
Ex situ remediation of contaminated sediments using mineral additives: Assessment of pollutant bioavailability with the Microtox solid phase test.png)
Chemosphere
ChemosphereIF0
PREAI
分享收藏Comparison of a logistic regression and Naive Bayes classifier in landslide susceptibility assessments: The influence of models complexity and training dataset size滑坡敏感性评估中逻辑回归和朴素贝叶斯分类器的比较: 模型复杂性和训练数据集大小的影响CATENA
CATENAIF5.7
PREAI
分享收藏
分享收藏分享收藏Validation of Ozone Monitoring Instrument UV Satellite Data Using Spectral and Broadband Surface Based Measurements at a Queensland Site利用光谱和宽带地面测量数据对昆士兰州某地的臭氧监测仪器紫外卫星数据进行验证
OAAI
OAAI分享收藏
查看更多内容