  In the contemporary big data realm, Deep Neural Networks (DNNs) are evolving
towards more complex architectures to achieve higher inference accuracy. Model
compression techniques can be leveraged to efficiently deploy such
compute-intensive architectures on resource-limited mobile devices. Such
methods comprise various hyper-parameters that require per-layer customization
to ensure high accuracy. Choosing such hyper-parameters is cumbersome as the
pertinent search space grows exponentially with model layers. This paper
introduces GeneCAI, a novel optimization method that automatically learns how
to tune per-layer compression hyper-parameters. We devise a bijective
translation scheme that encodes compressed DNNs to the genotype space. The
optimality of each genotype is measured using a multi-objective score based on
accuracy and number of floating point operations. We develop customized genetic
operations to iteratively evolve the non-dominated solutions towards the
optimal Pareto front, thus, capturing the optimal trade-off between model
accuracy and complexity. GeneCAI optimization method is highly scalable and can
achieve a near-linear performance boost on distributed multi-GPU platforms. Our
extensive evaluations demonstrate that GeneCAI outperforms existing rule-based
and reinforcement learning methods in DNN compression by finding models that
lie on a better accuracy-complexity Pareto curve.
