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Accelerating RNN-based Speech Enhancement on a Multi-Core MCU with Mixed FP16-INT8 Post-Training Quantization
Differently from a uniform 8-bit quantization that degrades the PESQ score by 0. 3 on average, the Mixed-Precision PTQ scheme leads to a low-degradation of only 0. 06, while achieving a 1. 4-1. 7x memory saving.
Leveraging Automated Mixed-Low-Precision Quantization for tiny edge microcontrollers
The severe on-chip memory limitations are currently preventing the deployment of the most accurate Deep Neural Network (DNN) models on tiny MicroController Units (MCUs), even if leveraging an effective 8-bit quantization scheme.
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