ML and DL Libraries Performance Optimization

A US-based semiconductor company asked Auriga to analyze and optimize Machine Learning and Deep Learning libraries’ performance on new processors.

Projects Highlights
  • Research and optimization of the ML/DL libraries’ performance: TensorFlow, Caffe, MXNET, scikit-learn, etc.
  • Low-level analysis of bottlenecks in basic mathematical algorithms (vector/matrix multiplication).
  • Parallel calculations features.
  • Comparison with state-of-the-art benchmarks of the leading hardware manufacturers.
Achieved Benefits
  • Neural networks libraries’ bottlenecks revealed in the course of detailed performance analysis.
  • Performance maximized due to optimal hardware/software configurations found.
  • Developed benchmarks demonstrate 20-30% higher performance for deep neural networks training on new processors compared to competing platforms.