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.

提出了一种用于 Gated Recurrent Units (GRU) 的模块化整数量化方案，通过遗传算法 (Genetic Algorithms) 优化 bit 宽度，以同时优化模型大小和准确性。在四个不同的顺序任务中，其混合精度解决方案在 Pareto 效率方面优于同质精度解决方案，实现模型大小减少 25% 至 55% 的同时，准确性与等效的 8 位同质模型可比。