A US-based semiconductor company asked Auriga to analyze and optimize Machine Learning and Deep Learning libraries’ performance on new processors.
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.
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.
Linux · Python · C++ · DLBench
DeepBench · Statistical analysis
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