Data Processing and Information Classification—An In-Memory Approach

Sensors

Mariagrazia Graziano¹  Milena Andrighetti²  Andrea Marchesin²  Giovanna Turvani²  Giulia Santoro²  Fabrizio Ottati²  Marco Vacca²  Maurizio Zamboni²  Massimo Ruo Roch² 

[1] Department of Applied Science and Technology (DISAT), Politecnico di Torino, Corso Castelfidardo 39, 10129 Torino, Italy;Department of Electronics and Telecommunication (DET), Politecnico di Torino, Corso Castelfidardo 39, 10129 Torino, Italy;

关键词: bitmap indexing;    processing in memory;    memory wall;    big data;    internet of things;   

DOI  :  10.3390/s20061681

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown