DiffRNN: Differential Verification of Recurrent Neural Networks

Understand

Recurrent neural networks (RNNs) such as Long Short Term Memory (LSTM) networks have become popular in a variety of applications such as image processing, data classification, speech recognition, and as controllers in autonomous systems.

• In practical settings, there is often a need to deploy such RNNs on resource-constrained platforms such as mobile phones or embedded devices.
• As the memory footprint and energy consumption of such components become a bottleneck, there is interest in compressing and optimizing such networks using a range of heuristic techniques.
• However, these techniques do not guarantee the safety of the optimized network, e.g., against adversarial inputs, or equivalence of the optimized and original networks.