SUBMITTED PUBLICATIONS

2018

QuantHD: A Quantization Framework for FPGA Acceleration of Hyperdimensional Computing

  • Work at University of California, San Diego
  • Group of prof. Tajana Rosing
  • We invented a new machine learning algorithm which can achieve on average 34.1x and 4.1x energy efficiency improvement on training and testing respectively, and 8.2x and 13.4x faster computing in training and testing. It provides similar classification accuracy as low-cost state-of-the-art Machine Learning algorithms

2018

LookHD: Acceleration of Hyperdimensional Computing Exploiting Computation Reuse

  • Work at University of California, San Diego
  • Group of prof. Tajana Rosing
  • We invented LookHD: a method which compresses a HD algorithm matrix into a single vector without significantly decreasing classification accuracy. This method is 2.2x faster and 4.1x more energy efficient, as compared to existing HD computing algorithms

2018

Application of QuantHD on semi-supervised machine learning

  • Work at University of California, San Diego
  • Group of prof. Tajana Rosing
  • Application of our new algorithm (QuantHD) on semi-supervised machine learning. We noticed an increase in performance, compared to state-of-the-art algorithms

2018

Global increase in atmospheric dust content over the last 300 years

  • Work at Niels Bohr Institute, University of Copenhagen
  • Group of prof.┬áDorthe Dahl-Jensen & prof. Paul Vallelonga
  • Discovery of a large increase in atmospheric dust content, which will have significant impact on our climate

PUBLICATIONS

05/2018

Particle shape accounts for instrumental discrepancy in ice core dust size distributions

  • Work at Niels Bohr Institute, University of Copenhagen
  • Group of prof.┬áDorthe Dahl-Jensen
  • Significant progress in measurement techniques for ice and climate science.
  • Statistical analysis of dust particle shapes and its reflection in a laser beam
  • 4th author (Statistical analysis)