.png){kind=icon}
返回
Exploiting Retraining-Based Mixed-Precision Quantization for Low-Cost DNN Accelerator Design
.png){kind=icon}
PRE
AI
DOI：10.1109/TNNLS.2020.3008996.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
For successful deployment of deep neural networks (DNNs) on resource-constrained devices, retraining-based quantization has been widely adopted to reduce the number of DRAM accesses. By properly setting training parameters, such as batch size and learning rate, bit widths of both weights and activations can be uniformly quantized down to 4 bit while maintaining full precision accuracy. In this article, we present a retraining-based mixed-precision quantization approach and its customized DNN accelerator to achieve high energy efficiency. In the proposed quantization, in the middle of retraining, an additional bit (extra quantization level) is assigned to the weights that have shown frequent switching between two contiguous quantization levels since it means that both quantization levels cannot help to reduce quantization loss. We also mitigate the gradient noise that occurs in the retraining process by taking a lower learning rate near the quantization threshold. For the proposed novel mixed-precision quantized network (MPQ-network), we have implemented a customized accelerator using a 65-nm CMOS process. In the accelerator, the proposed processing elements (PEs) can be dynamically reconfigured to process variable bit widths from 2 to 4 bit for both weights and activations. The numerical results show that the proposed quantization can achieve 1.37x better compression ratio for VGG-9 using CIFAR-10 data set compared with a uniform 4-bit (both weights and activations) model without loss of classification accuracy. The proposed accelerator also shows 1.29x of energy savings for VGG-9 using the CIFAR-10 data set over the state-of-the-art accelerator.
Keyword:
Deep neural network (DNN) accelerator
energy-efficient accelerator
model compression
quantization
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
8.9
论文数:
7.6K
被引数:
7.2W
机构
引用论文
.png){kind=icon}
查看更多内容