  Recent developments in deep neural networks (DNNs) have led to their adoption
in safety-critical systems, which in turn has heightened the need for
guaranteeing their safety. These safety properties of DNNs can be proven using
tools developed by the verification community. However, these tools are
themselves prone to implementation bugs and numerical stability problems, which
make their reliability questionable. To overcome this, some verifiers produce
proofs of their results which can be checked by a trusted checker. In this
work, we present a novel implementation of a proof checker for DNN
verification. It improves on existing implementations by offering numerical
stability and greater verifiability. To achieve this, we leverage two key
capabilities of Imandra, an industrial theorem prover: its support of infinite
precision real arithmetic and its formal verification infrastructure. So far,
we have implemented a proof checker in Imandra, specified its correctness
properties and started to verify the checker's compliance with them. Our
ongoing work focuses on completing the formal verification of the checker and
further optimizing its performance.
