Dynamic-range estimation
Dynamic-range estimation
复制标题
动态范围估计
DOI：
10.1109/tcad.2005.859507
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发表时间：
2006
影响因子：
2.9
通讯作者：
F. Najm
中科院分区：
文献类型：
--
作者：
Bin Wu;Jianwen Zhu;F. Najm
人们已经广泛认识到，可以利用应用程序的动态范围信息来减少处理器或特定应用集成电路的数据路径位宽，从而减少整个电路的面积、延迟和功耗。虽然最近提出的分析动态范围估计方法在运行时间方面比传统的基于分析的方法有明显的优势，但本文认为，对输入相关性和系统非线性的过于简单化的处理可能会导致显着的误差。本文介绍了Karhunen-Loegraveve展开、多项式混沌展开和独立分量分析这三种数学工具，它们不仅可以实现输入随机过程的正交分解，而且可以实现随机过程在具有乘法、除法和条件等复杂构造的线性和非线性系统中的传播。结果表明，当应用于有趣的非线性应用，如自适应滤波器、多项式滤波器和有理数滤波器时，该方法可以产生每个内部变量的完整准确统计，从而允许在电路性能和信噪比之间进行所需权衡的位宽合成
It has been widely recognized that the dynamic-range information of an application can be exploited to reduce the datapath bitwidth of either processors or application-specific integrated circuits and, therefore, the overall circuit area, delay, and power consumption. While recent proposals of analytical dynamic-range-estimation methods have shown significant advantages over the traditional profiling-based method in terms of runtime, it is argued here that the rather simplistic treatment of input correlation and system nonlinearity may lead to significant error. In this paper, three mathematical tools, namely Karhunen-Loegraveve expansion, polynomial chaos expansion, and independent component analysis are introduced, which enable not only the orthogonal decomposition of input random processes, but also the propagation of random processes through both linear and nonlinear systems with difficult constructs such as multiplications, divisions, and conditionals. It is shown that when applied to interesting nonlinear applications such as adaptive filters, polynomial filters, and rational filters, this method can produce complete accurate statistics of each internal variable, thereby allowing the synthesis of bitwidth with the desired trade off between circuit performance and signal-to-noise ratio