Abstract
In this paper, we present a case study on modeling and verification of Spiking Neural Networks (SNN) using Satisfiability Modulo Theory (SMT) solvers. SNN are special neural networks that have great similarity in their architecture and operation with the human brain. These networks have shown similar performance when compared to traditional networks with comparatively lesser energy requirement. We discuss different properties of SNNs and their functioning. We then use Z3, a popular SMT solver to encode the network and its properties. Specifically, we use the theory of Linear Real Arithmetic (LRA). Finally, we present a framework for verification and adversarial robustness analysis and demonstrate it on the Iris and MNIST benchmarks.
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Banerjee, S., Ghosh, S., Banerjee, A., Mohalik, S.K. (2023). SMT-Based Modeling and Verification of Spiking Neural Networks: A Case Study. In: Dragoi, C., Emmi, M., Wang, J. (eds) Verification, Model Checking, and Abstract Interpretation. VMCAI 2023. Lecture Notes in Computer Science, vol 13881. Springer, Cham. https://doi.org/10.1007/978-3-031-24950-1_2
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