Reducing Energy Consumption in Radio-astronomical and Ultrasound Imaging Tools

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 or brain both share the common requirements. That is, they must be processed in a local, real-time, and energy-efficient way. 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 utilizing 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 the potential of these highly-efficient technologies for use in radio astronomy and ultrasound brain imaging, delivering open-source libraries, innovation in limited-precision algorithms, and will develop a new tool to analyze energy efficiency. Ultimately, this will allow more (energy) efficient instruments to be built.


  • John Romein
  • Ben van Werkhoven
  • Rena Bakhshi
Contact person
Ben van Werkhoven