Reducing Energy Consumption in Radio-astronomical and Ultrasound Imaging Tools (RECRUIT)

When it comes to algorithms, technologies and energy constraints, imaging efforts in radio astronomy and medical ultrasound share fundamentally similar challenges, both near the edge and further downstream in processing pipelines. Although time and space scales are orders of magnitude apart, the associated data processing and enabling hardware to image galaxy and brain share the common requirements. That is, they must be processed in a local, real-time and energy-efficient way. 

What are we trying to achieve and how? 

In this project, ASTRON (the Netherlands Institute for Radio Astronomy) and CUBE (the Center for Ultrasound and Brain imaging at Erasmus MC) join forces to tackle HPC and energy-efficiency challenges by using new technologies and algorithmic improvements. The Adaptive Compute Acceleration Platform by Xilinx and Tensor Cores in NVIDIA GPUs are top examples of such enabling technologies. This project will unlock their potential for use in radio astronomy and ultrasound brain imaging, delivering open-source libraries, innovation in limited-precision algorithms, and a new tool to analyze energy efficiency. This will allow more (energy) efficient instruments to be built. 

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