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DTSTART;TZID=America/Denver:20231112T160600
DTEND;TZID=America/Denver:20231112T161100
UID:submissions.supercomputing.org_SC23_sess435_ws_pdswwip103@linklings.co
 m
SUMMARY:Domain-Aware Performant AI-Based Compression
DESCRIPTION:Boyuan Zhang (Indiana University); Luanzheng Guo, Nathan Talle
 nt, and Jan Strube (Pacific Northwest National Laboratory (PNNL)); and Din
 gwen Tao (Indiana University)\n\nMicroscopes play a critical role in scien
 tific discoveries. A grand challenge in microscopy-based research is to ma
 nage the significantly high data volume and velocity of data generation wh
 ile ensuring real-time analysis and closed-loop microscope operation. The 
 scanning transmission electron microscope (STEM) at PNNL can generate 1100
  frames per second with 128x128 pixels per frame, which leads to more than
  5 GB of data generation per minute. The future microscopes could well sup
 port even higher data rates. The current data processing solutions utilize
  sampling to manage such a high data volume and velocity. However, it's im
 portant to note that sampling can lead to the loss of critical information
 . Consequently, complete data processing and analytics are necessary. Data
  compression techniques that reduce data size while retaining key properti
 es on individual images appear to be a promising solution.\n\nTag: Data An
 alysis, Visualization, and Storage, Data Movement and Memory\n\nRegistrati
 on Category: Workshop Reg Pass\n\nSession Chairs: Suren Byna (The Ohio Sta
 te University, Lawrence Berkeley National Laboratory (LBNL)); Jay Lofstead
  (Sandia National Laboratories, University of New Mexico); Bing Xie (Micro
 soft Corporation); and Amelie Chi Zhou (Hong Kong Baptist University)\n\n
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