Two Heads are Better Than One: Neural Networks Quantization with 2D Hilbert Curve-based Output Representation

• URL: http://arxiv.org/abs/2405.14024v1
• Date: Wed, 22 May 2024 21:59:46 GMT
• Title: Two Heads are Better Than One: Neural Networks Quantization with 2D Hilbert Curve-based Output Representation
• Authors: Mykhailo Uss, Ruslan Yermolenko, Olena Kolodiazhna, Oleksii Shashko, Ivan Safonov, Volodymyr Savin, Yoonjae Yeo, Seowon Ji, Jaeyun Jeong,
• Abstract summary: We introduce a novel approach for DNN quantization that uses a redundant representation of DNN's output. We demonstrate that this mapping can reduce quantization error. Our approach can be applied to other tasks, including segmentation, object detection, and key-points prediction.
• Score: 3.4606942690643336
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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