Accelerated Synthesis of Neural Network-based Barrier Certificates Using Collaborative Learning
TimeThursday, July 14th5:10pm - 5:30pm PDT
Location3000, Level 3
Design Verification and Validation
DescriptionThis paper presents an effective two-stage approach named CL-BC, which fully exploits the parallel processing capability of underlying hardware to enable quick search for NN-based barrier certificates. In the first stage, CL-BC pre-trains an initial model based on partial sampling data. In the second stage, CL-BC conducts parallel learning on partitioned domains, where learned knowledge from different partitions can be aggregated to accelerate the convergence of a global NN model for barrier certificate synthesis. In this way, the overall synthesis time of an NN-based barrier certificate can be drastically reduced. Experimental results show the efficiency of our approach.