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首页> 外文期刊>Circuits and Systems II: Express Briefs, IEEE Transactions on >An Improved Signed Digit Representation Approach for Constant Vector Multiplication
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An Improved Signed Digit Representation Approach for Constant Vector Multiplication

机译:常数向量乘法的改进的带符号数字表示方法

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摘要

In this brief, the multiplier-free implementation of the constant vector multiplication is reexamined. A novel improved signed digit representation technique is proposed to overcome the two main drawbacks of the current multiplier-free techniques: 1) computational redundancy and 2) circuit irregularity. The fundamental difference between the proposed technique and the existing multiplier-free techniques is a novel optimization framework based on vector decomposition. The constant vector is decomposed into two terms: a “public” vector and a “private” matrix which consist of the public operations shared by all of the entries and the private operations of each individual entry, respectively. In this way, the overall data flow can be divided into two regular steps: multiplied by the “public” vector first and then by the “private” matrix. The computational complexity reduction task is then achieved by minimizing the length of the “public” vector and the number of operations in the “private” matrix. Experimental results demonstrate that the proposed technique outperforms the existing multiplier-free techniques in fewer operations and more regular circuit structure.
机译:在本摘要中,重新检查了常数向量乘法的无乘数实现。为了克服当前无乘数技术的两个主要缺点,提出了一种新颖的改进的有符号数字表示技术:1)计算冗余和2)电路不规则性。所提出的技术与现有的无乘数技术之间的根本区别是基于矢量分解的新型优化框架。常数向量被分解为两个术语:“公共”向量和“私有”矩阵,分别由所有条目共享的公共操作和每个单独条目的私有操作组成。这样,整个数据流可以分为两个常规步骤:首先乘以“公共”向量,然后再乘以“私有”矩阵。然后,通过最小化“公共”向量的长度和“私有”矩阵中的运算次数来实现降低计算复杂性的任务。实验结果表明,所提出的技术在更少的操作和更规则的电路结构上优于现有的无乘法器技术。

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  • 作者单位

    Key Laboratory of Intelligent Perception and Image Understanding, School of Electronic Engineering, Chinese Ministry of Education, Xidian University, Xidian University, Xi'an, Xi'an, ChinaChina;

    Key Laboratory of Intelligent Perception and Image Understanding, School of Electronic Engineering, Chinese Ministry of Education, Xidian University, Xidian University, Xi'an, Xi'an, ChinaChina;

    Key Laboratory of Intelligent Perception and Image Understanding, School of Electronic Engineering, Chinese Ministry of Education, Xidian University, Xidian University, Xi'an, Xi'an, ChinaChina;

    Key Laboratory of Intelligent Perception and Image Understanding, School of Electronic Engineering, Chinese Ministry of Education, Xidian University, Xidian University, Xi'an, Xi'an, ChinaChina;

    Key Laboratory of Intelligent Perception and Image Understanding, School of Electronic Engineering, Chinese Ministry of Education, Xidian University, Xidian University, Xi'an, Xi'an, ChinaChina;

    Key Laboratory of Intelligent Perception and Image Understanding, School of Electronic Engineering, Chinese Ministry of Education, Xidian University, Xidian University, Xi'an, Xi'an, ChinaChina;

    Key Laboratory of Intelligent Perception and Image Understanding, School of Electronic Engineering, Chinese Ministry of Education, Xidian University, Xidian University, Xi'an, Xi'an, ChinaChina;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Redundancy; Adders; Optimization; Matrix decomposition; Hardware; Search problems;

    机译:冗余加法器优化矩阵分解硬件搜索问题;

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