Presentation
EqBaB: Efficient Equivalence Verification for Compressed DNNs with Bound Propagation
DescriptionDeep neural network (DNN) compression methods help reduce the size and lower the complexity while preserving the performance. In this study, we present our EqBab, a branch-and-bound (BaB) based equivalence verification method, to evaluate compressed DNNs. We propose a merge framework, which computes the discrepancy between the reference and compressed DNNs and combines it with bound propagation to perform equivalence verification problems further. Compared to the reachability-based method, EqBaB can effectively handle a larger input domain with higher efficiency, where the complexity of the input increased by 170.67 times, but the total time only increased by 11.43 times. We also evaluate eight different compression methods using our approach in two datasets, demonstrating compression capability and discrepancies analysis.
Event Type
Networking
Work-in-Progress Poster
TimeSunday, June 226:00pm - 7:00pm PDT
LocationLevel 3 Lobby
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