Automatic Debug Video Series

Debug is the most resource intensive aspect of semiconductor development, typically requiring more than a third of total development time. Verifyter’s unique regression debug automation technology, based on Machine Learning (ML), typically shaves 75% off regression debug time. Used by many electronic systems and semiconductor companies today, Pindown easily fits into your simulation or emulation environment, accelerating the entire verification cycle while reducing manual debug intervention.

This video series highlights how Pindown leverages ML for bug prediction to identify failing code segments, simplifying bug triage, and ensuring successful regression runs. After an introduction, the series takes a deep dive into the technology and its application, before showing how the tool maybe be easily leveraged in many regression flows. The tool will be demonstrated in real system situations.

Overview
1. How does Automatic Debug work? How does Bug Prediction using Machine Learning work? (video 2:15)

Deep Dive
2. How to debug dependencies between code and tests (video 2:17)
3. Scaling: handle many tests, many commits (video 2:28)
4. Avoiding IT issues (video 2:28)
5. Handling Constrained Random Testing (video 3:14)

Adding Automatic Debug to your Test Flow
6. Debugging RTL simulations, software, tests on emulators, continuous integration (video 3:14)
7. Reduce the test run-time (video 3:17)
8. PinDown licensensing - test phase is free (video 0:47)

Demos
9. Debugging down to line granularity (video 3:13)
10. Debugging Large Systems (video 5:47)

Quiz
Test your Automatic Debug knowledge