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Data Processing and Information Classification—An In-Memory Approach

To live in the information society means to be surrounded by billions of electronic devices full of sensors that constantly acquire data. This enormous amount of data must be processed and classified. A solution commonly adopted is to send these data to server farms to be remotely elaborated. The dr...

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Autores principales: Andrighetti, Milena, Turvani, Giovanna, Santoro, Giulia, Vacca, Marco, Marchesin, Andrea, Ottati, Fabrizio, Ruo Roch, Massimo, Graziano, Mariagrazia, Zamboni, Maurizio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7146182/
https://www.ncbi.nlm.nih.gov/pubmed/32197308
http://dx.doi.org/10.3390/s20061681
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author Andrighetti, Milena
Turvani, Giovanna
Santoro, Giulia
Vacca, Marco
Marchesin, Andrea
Ottati, Fabrizio
Ruo Roch, Massimo
Graziano, Mariagrazia
Zamboni, Maurizio
author_facet Andrighetti, Milena
Turvani, Giovanna
Santoro, Giulia
Vacca, Marco
Marchesin, Andrea
Ottati, Fabrizio
Ruo Roch, Massimo
Graziano, Mariagrazia
Zamboni, Maurizio
author_sort Andrighetti, Milena
collection PubMed
description To live in the information society means to be surrounded by billions of electronic devices full of sensors that constantly acquire data. This enormous amount of data must be processed and classified. A solution commonly adopted is to send these data to server farms to be remotely elaborated. The drawback is a huge battery drain due to high amount of information that must be exchanged. To compensate this problem data must be processed locally, near the sensor itself. But this solution requires huge computational capabilities. While microprocessors, even mobile ones, nowadays have enough computational power, their performance are severely limited by the Memory Wall problem. Memories are too slow, so microprocessors cannot fetch enough data from them, greatly limiting their performance. A solution is the Processing-In-Memory (PIM) approach. New memories are designed that can elaborate data inside them eliminating the Memory Wall problem. In this work we present an example of such a system, using as a case of study the Bitmap Indexing algorithm. Such algorithm is used to classify data coming from many sources in parallel. We propose a hardware accelerator designed around the Processing-In-Memory approach, that is capable of implementing this algorithm and that can also be reconfigured to do other tasks or to work as standard memory. The architecture has been synthesized using CMOS technology. The results that we have obtained highlights that, not only it is possible to process and classify huge amount of data locally, but also that it is possible to obtain this result with a very low power consumption.
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spelling pubmed-71461822020-04-15 Data Processing and Information Classification—An In-Memory Approach Andrighetti, Milena Turvani, Giovanna Santoro, Giulia Vacca, Marco Marchesin, Andrea Ottati, Fabrizio Ruo Roch, Massimo Graziano, Mariagrazia Zamboni, Maurizio Sensors (Basel) Article To live in the information society means to be surrounded by billions of electronic devices full of sensors that constantly acquire data. This enormous amount of data must be processed and classified. A solution commonly adopted is to send these data to server farms to be remotely elaborated. The drawback is a huge battery drain due to high amount of information that must be exchanged. To compensate this problem data must be processed locally, near the sensor itself. But this solution requires huge computational capabilities. While microprocessors, even mobile ones, nowadays have enough computational power, their performance are severely limited by the Memory Wall problem. Memories are too slow, so microprocessors cannot fetch enough data from them, greatly limiting their performance. A solution is the Processing-In-Memory (PIM) approach. New memories are designed that can elaborate data inside them eliminating the Memory Wall problem. In this work we present an example of such a system, using as a case of study the Bitmap Indexing algorithm. Such algorithm is used to classify data coming from many sources in parallel. We propose a hardware accelerator designed around the Processing-In-Memory approach, that is capable of implementing this algorithm and that can also be reconfigured to do other tasks or to work as standard memory. The architecture has been synthesized using CMOS technology. The results that we have obtained highlights that, not only it is possible to process and classify huge amount of data locally, but also that it is possible to obtain this result with a very low power consumption. MDPI 2020-03-18 /pmc/articles/PMC7146182/ /pubmed/32197308 http://dx.doi.org/10.3390/s20061681 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Andrighetti, Milena
Turvani, Giovanna
Santoro, Giulia
Vacca, Marco
Marchesin, Andrea
Ottati, Fabrizio
Ruo Roch, Massimo
Graziano, Mariagrazia
Zamboni, Maurizio
Data Processing and Information Classification—An In-Memory Approach
title Data Processing and Information Classification—An In-Memory Approach
title_full Data Processing and Information Classification—An In-Memory Approach
title_fullStr Data Processing and Information Classification—An In-Memory Approach
title_full_unstemmed Data Processing and Information Classification—An In-Memory Approach
title_short Data Processing and Information Classification—An In-Memory Approach
title_sort data processing and information classification—an in-memory approach
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7146182/
https://www.ncbi.nlm.nih.gov/pubmed/32197308
http://dx.doi.org/10.3390/s20061681
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