This paper presents a novel approach for post-training quantization (PTQ) of reparameterized networks. We propose the Coarse & Fine Weight Splitting (CFWS) method to reduce quantization error in weight distribution and develop an improved KL metric for optimal quantization scales of activations. Our...
Post-training quantization(PTQ) 工作流理解 目前神经网络在许多前沿领域的应用取得了较大进展,但经常会带来很高的计算成本,对内存带宽和算力要求高。另外降低神经网络的功率和时延在现代网络集成到边缘设备时也极其关键,在这些场景中模型推理具有严格的功率和计算要求。神经网络量化是解决上述问题有效方法之一,但是模型量化...
The well-established uniform scheme for post-training quantization achieves satisfactory results by converting neural networks from full-precision to 8-bit fixed-point integers. However, it suffers from significant performance degradation when quantizing to lower bit-widths. In this paper, we propose a...
In this paper, we introduced VLCQ: post-training quantization with variable-length encoding; A data-free method for quantization, which requires no access to the original full dataset. VLCQ leverages from the normal distribution of weight, by dividing weights into two regions, where each region ...
Quantization is one of the most popular methods to compress the model for meeting the performance limitations. As only a small amount of calibration data is required, post-training quantization (PTQ) is more suitable for protecting privacy than quantization-aware training(QAT). However, PTQ often ...
Existing post-training quantization methods leverage value redistribution or specialized quantizers to address the non-normal distribution in ViTs. However, without considering the asymmetry in activations and relying on hand-crafted settings, these methods often struggle to maintain performance under low-...
Bit-shrinking: Limiting Instantaneous Sharpness for Improving Post-training Quantization Chen Lin1 Bo Peng1 Zheyang Li1 Wenming Tan1 Ye Ren1 Jun Xiao2 Shiliang Pu1 1 Hikvision Research Institute 2Zhe Jiang University linchen7@hikvision.com pengbo7@hikvision.co...
However, exist- ing works focus on quantization-aware training, which re- quires complete dataset and expensive computational over- head. In this paper, we study post-training quantization (PTQ) for image super resolution using only a few unla- beled calibration im...
ECCV2022 Paper - Fine-grained Data Distribution Alignment for Post-Training Quantizationpaper Requirements Python >= 3.7.10 Pytorch >= 1.7.0 Torchvision >= 0.4.0 Reproduce the Experiment Results The pre-trained model will be downloaded automatically. If the download process fails, please use the ...
1、quantization: quantization-aware-training(QAT), High-Bit(>2b)(DoReFa/Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference)、Low-Bit(≤2b)/Ternary and Binary(TWN/BNN/XNOR-Net); post-training-quantization(PTQ), 8-bit(tensorrt); 2、 pruning: normal、reg...