  AI's widespread integration has led to neural networks (NNs) deployment on
edge and similar limited-resource platforms for safety-critical scenarios. Yet,
NN's fragility raises concerns about reliable inference. Moreover, constrained
platforms demand compact networks. This study introduces VeriCompress, a tool
that automates the search and training of compressed models with robustness
guarantees. These models are well-suited for safety-critical applications and
adhere to predefined architecture and size limitations, making them deployable
on resource-restricted platforms. The method trains models 2-3 times faster
than the state-of-the-art approaches, surpassing relevant baseline approaches
by average accuracy and robustness gains of 15.1 and 9.8 percentage points,
respectively. When deployed on a resource-restricted generic platform, these
models require 5-8 times less memory and 2-4 times less inference time than
models used in verified robustness literature. Our comprehensive evaluation
across various model architectures and datasets, including MNIST, CIFAR, SVHN,
and a relevant pedestrian detection dataset, showcases VeriCompress's capacity
to identify compressed verified robust models with reduced computation overhead
compared to current standards. This underscores its potential as a valuable
tool for end users, such as developers of safety-critical applications on edge
or Internet of Things platforms, empowering them to create suitable models for
safety-critical, resource-constrained platforms in their respective domains.
