...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Circuits and Systems I: Regular Papers, IEEE Transactions on >Retrain-Less Weight Quantization for Multiplier-Less Convolutional Neural Networks
【24h】

Retrain-Less Weight Quantization for Multiplier-Less Convolutional Neural Networks

机译：乘法重量量化倍数较少的卷积神经网络

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

This article presents an approximate signed digit representation (ASD) which quantizes the weights of convolutional neural networks (CNNs) in order to make multiplier-less CNNs without performing any retraining process. Unlike the existing methods that necessitate retraining for weight quantization, the proposed method directly converts full-precision weights of CNN models into low-precision ones, attaining accuracy comparable to that of full-precision models on the Image classification tasks without going through retraining. Therefore, it is effective in saving the retraining time as well as the related computational cost. As the proposed method simplifies the weights to have up to two non-zero digits, multiplication can be realized with only add and shift operations, resulting in a speed-up of inference time and a reduction of energy consumption and hardware complexity. Experiments conducted for famous CNN architectures, such as AlexNet, VGG-16, ResNet-18 and SqueezeNet, show that the proposed method reduces the model size by 73% at the cost of a little increase of error rate, which ranges from 0.09% to 1.5% on ImageNet dataset. Compared to the previous architecture built with multipliers, the proposed multiplier-less convolution architecture reduces the critical-path delay by 52% and mitigates the hardware complexity and power consumption by more than 50%.
机译：本文呈现了近似有符号的符号符号表示（ASD），其量化卷积神经网络（CNNS）的权重，以便在不执行任何再掠过程的情况下使乘数的CNN制作。与需要刷新重量量化的现有方法不同，所提出的方法直接将CNN模型的全精度重量转换为低精度，实现与图像分类任务上的全精度模型的精度相当，而不通过再培训。因此，它可以有效地保存再润滑时间以及相关的计算成本。由于所提出的方法简化了最多两个非零位的权重，只有添加和移位操作可以实现乘法，导致推理时间的加速和能量消耗和硬件复杂度的减少。针对着名的CNN架构进行的实验，例如AlexNet，VGG-16，Reset-18和Squeezenet，表明该方法以略微增加的成本降低了73％的模型大小，从0.09％到0.09％在想象中数据集1.5％。与具有乘数构建的以前的架构相比，所提出的乘法器较少的卷积架构将临界路径延迟减少52％并减轻硬件复杂性和功耗超过50％。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号