  QNNVerifier is the first open-source tool for verifying implementations of
neural networks that takes into account the finite word-length (i.e.
quantization) of their operands. The novel support for quantization is achieved
by employing state-of-the-art software model checking (SMC) techniques. It
translates the implementation of neural networks to a decidable fragment of
first-order logic based on satisfiability modulo theories (SMT). The effects of
fixed- and floating-point operations are represented through direct
implementations given a hardware-determined precision. Furthermore, QNNVerifier
allows to specify bespoke safety properties and verify the resulting model with
different verification strategies (incremental and k-induction) and SMT
solvers. Finally, QNNVerifier is the first tool that combines invariant
inference via interval analysis and discretization of non-linear activation
functions to speed up the verification of neural networks by orders of
magnitude. A video presentation of QNNVerifier is available at
https://youtu.be/7jMgOL41zTY
