Summary
In the contemporary digital era, relational databases play a pivotal role in business applications, facilitating the management and analysis of intricate data sets in diverse sectors including healthcare, finance, and social media platforms. These databases comprise relations represented as sets of records and attributes, allowing for detailed data scrutiny and insightful business decision-making. However, the prevalent model of data analysis is beleaguered by lengthy execution times and considerable energy consumption due to the separation of data processing and storage, necessitating hefty computational investments.
Addressing this, we propose an innovative approach leveraging processing-in-memory (PIM) techniques, specifically bulk-bitwise PIM, to hasten analytical processing in relational databases. Grounded in emergent nonvolatile memristive memory technologies, this method capitalizes on memory cell arrays for concurrent data processing and result storage, markedly diminishing data movement and consequently, time and energy expenditure. Our endeavor is to craft the memristive memory processing unit (mMPU), originally developed during the PI's ERC StG Real-PIM-System project, adept at accelerating relational database analysis, promising to deliver a computing system that is tenfold faster and a hundred times more energy-efficient at a fraction of the current cost, revolutionizing data analysis and offering substantial savings in both time and financial resources.
Addressing this, we propose an innovative approach leveraging processing-in-memory (PIM) techniques, specifically bulk-bitwise PIM, to hasten analytical processing in relational databases. Grounded in emergent nonvolatile memristive memory technologies, this method capitalizes on memory cell arrays for concurrent data processing and result storage, markedly diminishing data movement and consequently, time and energy expenditure. Our endeavor is to craft the memristive memory processing unit (mMPU), originally developed during the PI's ERC StG Real-PIM-System project, adept at accelerating relational database analysis, promising to deliver a computing system that is tenfold faster and a hundred times more energy-efficient at a fraction of the current cost, revolutionizing data analysis and offering substantial savings in both time and financial resources.
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| Web resources: | https://cordis.europa.eu/project/id/101157452 |
| Start date: | 01-10-2024 |
| End date: | 31-03-2026 |
| Total budget - Public funding: | - 150 000,00 Euro |
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Original description
In the contemporary digital era, relational databases play a pivotal role in business applications, facilitating the management and analysis of intricate data sets in diverse sectors including healthcare, finance, and social media platforms. These databases comprise relations represented as sets of records and attributes, allowing for detailed data scrutiny and insightful business decision-making. However, the prevalent model of data analysis is beleaguered by lengthy execution times and considerable energy consumption due to the separation of data processing and storage, necessitating hefty computational investments.Addressing this, we propose an innovative approach leveraging processing-in-memory (PIM) techniques, specifically bulk-bitwise PIM, to hasten analytical processing in relational databases. Grounded in emergent nonvolatile memristive memory technologies, this method capitalizes on memory cell arrays for concurrent data processing and result storage, markedly diminishing data movement and consequently, time and energy expenditure. Our endeavor is to craft the memristive memory processing unit (mMPU), originally developed during the PI's ERC StG Real-PIM-System project, adept at accelerating relational database analysis, promising to deliver a computing system that is tenfold faster and a hundred times more energy-efficient at a fraction of the current cost, revolutionizing data analysis and offering substantial savings in both time and financial resources.
Status
SIGNEDCall topic
ERC-2023-POCUpdate Date
12-03-2024
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