.png){kind=icon}
返回
A Hardware-Efficient Sigmoid Function With Adjustable Precision for a Neural Network System
.png){kind=icon}
PRE
AI
DOI：10.1109/TCSII.2015.2456531.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
A hardware-efficient sigmoid function calculator with adjustable precision for neural network and deep-learning applications is proposed in this brief. By adopting the bit-plane format of the input and output values, the computational latency of the processing time can be dynamically reduced according to the user configuration. To reduce the hardware cost, the coefficients used to calculate the sigmoid value can be shared for multiple calculators without any structural hazard. In addition, the restricted constraint is applied in the coefficients' training stage to further simplify the computation in the calculation stage with a negligible quality loss. A test module is designed for the proposal and operated at 300 MHz to achieve 75 million sigmoid calculations per second. Implemented in 90-nm CMOS technology, the core of the calculator costs 1663 gates, and a 1-kb globally shared memory is used to store the coefficients.
Keyword:
Adjustable precision
deep learning
hardware efficient
neural network
sigmoid function
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
I
IF:
4.9
论文数:
8.8K
被引数:
2.5W
机构
引用论文
Challenges and Advancements in Additive Manufacturing of Nylon and Nylon Composite Materials: A Comprehensive Analysis of Mechanical Properties, Morphology, and Recent Progress尼龙及尼龙复合材料增材制造中的挑战与进展：机械性能、形貌及近期进展的全面分析
PREAI
分享收藏分享收藏Implementing Relative Deflection of Adjacent Blocks in Model Calibration of Masonry Arch Bridges
PREAI
分享收藏Interplay between anisotropic spin texture and large gap topological insulating phases in functionalized MXenes功能化mxene中各向异性自旋织构与大间隙拓扑绝缘相之间的相互作用
PREAI
分享收藏Highly Strained para-Phenylene-Bridged Macrocycles from Unstrained 1,4-Diketo Macrocycles.png)
Synlett
SynlettIF0
PREAI
分享收藏
查看更多内容