  As neural networks are trained to be deeper and larger, the scalability of
neural network analyzers is urgently required. The main technical insight of
our method is modularly analyzing neural networks by segmenting a network into
blocks and conduct the analysis for each block. In particular, we propose the
network block summarization technique to capture the behaviors within a network
block using a block summary and leverage the summary to speed up the analysis
process. We instantiate our method in the context of a CPU-version of the
state-of-the-art analyzer DeepPoly and name our system as Bounded-Block Poly
(BBPoly). We evaluate BBPoly extensively on various experiment settings. The
experimental result indicates that our method yields comparable precision as
DeepPoly but runs faster and requires less computational resources. For
example, BBPoly can analyze really large neural networks like SkipNet or ResNet
which contain up to one million neurons in less than around 1 hour per input
image, while DeepPoly needs to spend even 40 hours to analyze one image.
