SubsidieMeestersAccelerating Relational Databases with Real Memristive Processing-in-Memory
This project aims to enhance relational database analysis speed and energy efficiency by developing a memristive memory processing unit using processing-in-memory techniques.
Projectdetails
Introduction
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.
Challenges in Data Analysis
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.
Proposed Solution
Addressing this, we propose an innovative approach leveraging processing-in-memory (PIM) techniques, specifically bulk-bitwise PIM, to hasten analytical processing in relational databases.
Methodology
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.
Project Goals
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.
Expected Outcomes
This project promises to deliver a computing system that is:
- Tenfold faster
- A hundred times more energy-efficient
- At a fraction of the current cost
This innovation aims to revolutionize data analysis and offer substantial savings in both time and financial resources.
Financiële details & Tijdlijn
Financiële details
| Subsidiebedrag | € 150.000 |
| Totale projectbegroting | € 150.000 |
Tijdlijn
| Startdatum | 1-10-2025 |
| Einddatum | 31-3-2027 |
| Subsidiejaar | 2025 |
Partners & Locaties
Projectpartners
- TECHNION - ISRAEL INSTITUTE OF TECHNOLOGYpenvoerder
Land(en)
Vergelijkbare projecten binnen European Research Council
| Project | Regeling | Bedrag | Jaar | Actie |
|---|---|---|---|---|
Real Processing in Phase Change MemoryThe project aims to develop and commercialize a memristive memory processing unit (mMPU) using phase change memory to enhance computer performance and energy efficiency for various applications. | ERC Proof of... | € 150.000 | 2022 | Details |
ANalogue In-Memory computing with Advanced device TEchnologyThe project aims to develop closed-loop in-memory computing (CL-IMC) technology to significantly reduce energy consumption in data processing while maintaining high computational efficiency. | ERC Advanced... | € 2.498.868 | 2023 | Details |
Memristive Neurons and Synapses for Neuromorphic Edge ComputingMEMRINESS aims to develop compact, power-efficient Spiking Neural Networks using memristive technology for enhanced collaborative learning on edge systems. | ERC Starting... | € 1.499.488 | 2022 | Details |
Memristive self-organizing dendrite networks for brain-inspired computingThe MEMBRAIN project aims to develop self-organizing memristive nanonetworks for efficient, nature-inspired computing that mimics biological neural circuits, enhancing adaptability and intelligence. | ERC Starting... | € 1.487.500 | 2025 | Details |
Heterogeneous integration of imprecise memory devices to enable learning from a very small volume of noisy dataThe DIVERSE project aims to develop energy-efficient cognitive computing inspired by insect nervous systems, utilizing low-endurance resistive memories for real-time decision-making in noisy environments. | ERC Consolid... | € 2.874.335 | 2022 | Details |
Real Processing in Phase Change Memory
The project aims to develop and commercialize a memristive memory processing unit (mMPU) using phase change memory to enhance computer performance and energy efficiency for various applications.
ANalogue In-Memory computing with Advanced device TEchnology
The project aims to develop closed-loop in-memory computing (CL-IMC) technology to significantly reduce energy consumption in data processing while maintaining high computational efficiency.
Memristive Neurons and Synapses for Neuromorphic Edge Computing
MEMRINESS aims to develop compact, power-efficient Spiking Neural Networks using memristive technology for enhanced collaborative learning on edge systems.
Memristive self-organizing dendrite networks for brain-inspired computing
The MEMBRAIN project aims to develop self-organizing memristive nanonetworks for efficient, nature-inspired computing that mimics biological neural circuits, enhancing adaptability and intelligence.
Heterogeneous integration of imprecise memory devices to enable learning from a very small volume of noisy data
The DIVERSE project aims to develop energy-efficient cognitive computing inspired by insect nervous systems, utilizing low-endurance resistive memories for real-time decision-making in noisy environments.
Vergelijkbare projecten uit andere regelingen
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|---|---|---|---|---|
Accelerating Datacentre performance through Memory Chips to efficiently manage the Big Data AgeUPMEM's Processing-In-Memory technology enhances server efficiency by performing calculations within memory chips, achieving up to 20x speed and 10x energy savings for Big Data and AI applications. | EIC Accelerator | € 2.496.229 | 2022 | Details |
Processing-in-memory architectures and programming libraries for bioinformatics algorithmsThis project aims to enhance genomics research by developing energy-efficient, cost-effective edge computing solutions using processing-in-memory technologies for high-throughput sequencing data analysis. | EIC Pathfinder | € 1.966.665 | 2022 | Details |
Ultra-High Speed memories for unprecedented cloud-computing performanceXenergic aims to revolutionize SRAM design for IoT and high-performance applications, achieving up to 90% energy savings and 3x speed, while seeking EIC Accelerator investment for market readiness. | EIC Accelerator | € 2.499.999 | 2022 | Details |
Hybrid electronic-photonic architectures for brain-inspired computingHYBRAIN aims to develop a brain-inspired hybrid architecture combining integrated photonics and unconventional electronics for ultrafast, energy-efficient edge AI inference. | EIC Pathfinder | € 1.672.528 | 2022 | Details |
Green SELf-Powered NEuromorphic Processing EnGines with Integrated VisuAl and FuNCtional SEnsingELEGANCE aims to develop eco-friendly, light-operated processing technology for energy-efficient IoT applications, utilizing sustainable materials to minimize electronic waste and environmental impact. | EIC Pathfinder | € 3.100.934 | 2024 | Details |
Accelerating Datacentre performance through Memory Chips to efficiently manage the Big Data Age
UPMEM's Processing-In-Memory technology enhances server efficiency by performing calculations within memory chips, achieving up to 20x speed and 10x energy savings for Big Data and AI applications.
Processing-in-memory architectures and programming libraries for bioinformatics algorithms
This project aims to enhance genomics research by developing energy-efficient, cost-effective edge computing solutions using processing-in-memory technologies for high-throughput sequencing data analysis.
Ultra-High Speed memories for unprecedented cloud-computing performance
Xenergic aims to revolutionize SRAM design for IoT and high-performance applications, achieving up to 90% energy savings and 3x speed, while seeking EIC Accelerator investment for market readiness.
Hybrid electronic-photonic architectures for brain-inspired computing
HYBRAIN aims to develop a brain-inspired hybrid architecture combining integrated photonics and unconventional electronics for ultrafast, energy-efficient edge AI inference.
Green SELf-Powered NEuromorphic Processing EnGines with Integrated VisuAl and FuNCtional SEnsing
ELEGANCE aims to develop eco-friendly, light-operated processing technology for energy-efficient IoT applications, utilizing sustainable materials to minimize electronic waste and environmental impact.