Presentation
Predicting Computer Resource Needs using Machine Learning and Conventional Design
DescriptionToday, the semiconductor design industry is centered around the use of EDA tools. These tools provide the necessary information and automation for a design engineer to do their work effectively. The automation of design processes is especially significant and has been key to the success of the industry. However, process automation comes at the cost of large compute resource requirements. These requirements will only increase as the industry continues to automate more processes. Therefore, the way a semiconductor design company manages their compute resources is and will continue to be essential to their success.
This presentation describes the details of two systems developed for predicting EDA tool resource usage. The first relying on a more conventionally engineered "recently used" algorithm and the second centered around a machine learning framework. Covered topics will include comparisons of algorithm complexity and accuracy in key compute resources such as memory usage.
This presentation describes the details of two systems developed for predicting EDA tool resource usage. The first relying on a more conventionally engineered "recently used" algorithm and the second centered around a machine learning framework. Covered topics will include comparisons of algorithm complexity and accuracy in key compute resources such as memory usage.
Event Type
Engineering Track Poster
TimeMonday, June 245:00pm - 6:00pm PDT
LocationLevel 2 Exhibit Hall
Back-End Design
Embedded Systems
Front-End Design
IP