Despite the tremendous advances in Integrated Circuit (IC) Design, there has been only incremental innovation in Design Verification over the past few decades. Design Verification (DV), the process of verifying that an IC functions as intended, takes up more than 50% of the time and cost of designing an IC (Reference: Research study by Siemens). Costs of DV are increasing, and, the time-to-market for new IC projects are slipping due to DV. To meet the growing demand for IC's we need to find innovative ways to speed up verification and reduce the associated costs. Additionally, as the research highlights, DV requires a significant amount of engineering talent and, the demand for DV Engineers grew at a 6.8% CAGR. There are not enough DV engineers being produced to meet this demand. Using innovative Machine Learning approaches presents significant opportunities to accelerate innovation in DV.
The Objective of this DV Challenge is for participants to use innovative Machine Learning techniques to speed up verification and find bugs faster. This goal in this challenge is to maximize the average FIFO depths in a MESI Cache Controller Design. There are 4 FIFO Queues in this Cache Controller, one for each CPU. Each FIFO queue can hold up to 16 entries. The goal is to maximize the number of entries in each FIFO. Simply put, the higher average FIFO depth across all 4 queues the better are our chances of finding hard bugs. Participants can tune the Machine Learning Hyper-parameters and DV Knobs (settings) to increase the FIFO depths. VerifAI's Machine Learning Platform helps DV Engineers speed up verification significantly. Finding bugs faster and Speeding up DV, reduces costs and improves time to market significantly.
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VerifAI will publish and promote top-rated entries through its network of affiliates, industry publications, blog posts, and social networks
Participants will receive a certificate of participation from VerifAI
Participant registration opens
Participant registration closes
DV Challenge #1 closes
Prize is announced