Case Study: US-based Semiconductor Company achieves 20-30% higher deep neural network training performance with Auriga ML/DL optimization

US-based Semiconductor Company - Customer Case Study

Auriga worked with a US-based Semiconductor Company that needed to analyze and improve the performance of machine learning and deep learning libraries on new processors. The customer wanted to understand how frameworks such as TensorFlow, Caffe, MXNet, and scikit-learn behaved on its hardware and identify the main bottlenecks in their ML/DL workloads.

Auriga performed low-level performance analysis and optimization of the libraries, including vector/matrix multiplication, parallel computation features, and benchmarking against leading hardware vendors’ platforms. As a result, Auriga uncovered key neural network bottlenecks, found optimal hardware/software configurations, and delivered benchmarks showing 20–30% higher deep neural network training performance on the new processors versus competing platforms.