Imagine if your computer could think inside its own memory instead of moving data back and forth like a courier. This project builds a special kind of hardware that does exactly that for genetic data. It makes analyzing DNA and RNA much faster and uses far less electricity than today's giant data centers.
Current genomic data analysis relies on energy-hungry data centers and requires transferring massive files over the internet, which is slow and expensive.
The project is building processing-in-memory (PIM) hardware architectures and specialized programming libraries for bioinformatics algorithms.
If you are a diagnostic clinic dealing with slow rare disease diagnosis — this project developed PIM architectures that enable ultra-fast bioinformatics analysis. This allows for identifying genetic mutations locally without needing a stable internet connection to a cloud center.
If you are a research lab dealing with the high energy costs of large-scale sequencing studies — this project developed co-designed algorithms and hardware that reduce energy consumption. This makes population genomics and cancer mutation discovery more cost-efficient.
If you are a health agency dealing with outbreak detection for viruses like COVID-19 in remote areas — this project developed edge computing capabilities. This removes the need to transfer massive amounts of data to distant computer centers.
Based on available project data, it reduces costs by eliminating the need for expensive cloud platforms and reducing the energy spent moving data between processing units and memory.
Based on available project data, the project aims to impact all computing environments, including large-scale cloud platforms and edge computing devices.
Based on available project data, the project is developing programming libraries and hardware architectures, but specific licensing terms are not provided.
The project focuses on processing-in-memory (PIM) architectures that couple processing capability directly with memory and storage devices.
The project period runs from 2022-05-01 to 2026-10-31.
The consortium consists of 8 partners across 4 countries (CH, FR, IL, TR). It shows a healthy mix of academic and commercial interests with 4 universities, 2 research organizations, and 2 industry partners (including 1 SME), resulting in a 25% industry ratio.
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