ML For Hardware Design Interpretability: Challenges and Opportunities

Raymond Baartmans, Andrew Ensinger, Victor Agostinelli +1

The increasing size and complexity of machine learning (ML) models have driven the growing need for custom hardware accelerators capable of efficiently supporting ML workloads. How…