  Quantization is widely used to increase deep neural networks' (DNN) memory,
computation, and power efficiency. Various techniques, such as post-training
quantization and quantization-aware training, have been proposed to improve
quantization quality. We introduce a novel approach for DNN quantization that
uses a redundant representation of DNN's output. We represent the target
quantity as a point on a 2D parametric curve. The DNN model is modified to
predict 2D points that are mapped back to the target quantity at a
post-processing stage. We demonstrate that this mapping can reduce quantization
error. For the low-order parametric Hilbert curve, Depth-From-Stereo task, and
two models represented by U-Net architecture and vision transformer, we
achieved a quantization error reduction by about 5 times for the INT8 model at
both CPU and DSP delegates. This gain comes with a minimal inference time
increase (less than 7%). Our approach can be applied to other tasks, including
segmentation, object detection, and key-points prediction.
