  Despite the recent advances in model compression techniques for deep neural
networks, deploying such models on ultra-low-power embedded devices still
proves challenging. In particular, quantization schemes for Gated Recurrent
Units (GRU) are difficult to tune due to their dependence on an internal state,
preventing them from fully benefiting from sub-8bit quantization. In this work,
we propose a modular integer quantization scheme for GRUs where the bit width
of each operator can be selected independently. We then employ Genetic
Algorithms (GA) to explore the vast search space of possible bit widths,
simultaneously optimising for model size and accuracy. We evaluate our methods
on four different sequential tasks and demonstrate that mixed-precision
solutions exceed homogeneous-precision ones in terms of Pareto efficiency. In
our results, we achieve a model size reduction between 25% and 55% while
maintaining an accuracy comparable with the 8-bit homogeneous equivalent.
