Post-training quantizationNeural network quantization enables the deployment of large models on resource-constrained devices. Current post-training quantization methods fall short in terms of accuracy for INT4 (or lower) but provide reasonable accuracy for INT8 (or above). In this work, we study ...
引言 Product quantization,国内有人直译为乘积量化,这里的乘积是指笛卡尔积(Cartesian product),意思是指把原来的向量空间分解为若干个低维向量空间的笛卡尔积,并对分解得到的低维向量空间分别做量化(quantization)。这样每个向量就能由多个低维空间的量化...
3.2 Quantization Aware Training 训练后量化的精度高于训练时量化。 作者还给出了基于TensorFlow的量化...,N_l=256),我们需要2个参数:量化尺度Δ\DeltaΔ和零点 zzz。尺度决定量化步长,浮点数0映射到零点,且无误差(应该是通过round操作对零点进行微调,确保精确量化...
Loss aware post-training quantization Neural network quantization enables the deployment of large models on resource-constrained devices. Current post-training quantization methods fall short i... Y Nahshan,B Chmiel,C Baskin,... - 《Machine Learning》 被引量: 0发表: 2021年 Quantization Friendly Mo...
paper170:CVPR2020网络量化和压缩 Adaptive loss-aware quantization for multi-bi networks 要点简介 1、概括性介绍 1)无论是图像压缩/视频压缩,还是其他典型的任务,因为参数的全量化精度开销很大,所以部署问题就变得很关键。 2)神经网络量化本身就是为了追求压缩比和性能之间的平衡,量化分为均匀量化、非均匀量化和细...
quantization { delay: 30000 activation_bits: 8 weight_bits: 8 } } For transfering learning,I download mobilenet_v2_1.0_224 fromhttps://github.com/tensorflow/models/tree/master/research/slim/nets/mobilenet When training,Then error reported. ...
Methods, apparatus, systems and articles of manufacture for loss-error-aware quantization of a low-bit neural network are disclosed. An example apparatus includes a network weight partitioner to partition unquantized network weights of a first network model into a first group to be quantized and a...
quantization to quantize activation sets into ultra-low-bit versions with given bit-width values, to optimize the NN with respect to a loss function that is based on the full-precision NN model, and to perform a loss-error-aware weight quantization to quantize weight sets into ultra-low-bit...