most citedResource-Efficient Speech Quality Prediction through Quantization Aware Training and Binary Activation Maps

2 citations

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2 papers

cs.SD2024★ 2 cited

Resource-Efficient Speech Quality Prediction through Quantization Aware Training and Binary Activation Maps

Mattias Nilsson, Riccardo Miccini, Clément Laroche +2

As speech processing systems in mobile and edge devices become more commonplace, the demand for unintrusive speech quality monitoring increases. Deep learning methods provide high-…

cs.LG2024★ 1 cited

Towards a tailored mixed-precision sub-8-bit quantization scheme for Gated Recurrent Units using Genetic Algorithms

Riccardo Miccini, Alessandro Cerioli, Clément Laroche +3

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 particu…