Dynamic range estimation for systems with control-flow structures

Bin Wu
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引用次数: 5

Abstract

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 ASICs, and therefore the overall circuit area, delay and power consumption. Many analytical approaches are proposed for dynamic range estimation. However, because of the intractable nature of control-flow structures, all currently available methods consider only the systems consisting of pure dataflow structures/operations, while the general digital applications always contain some control-flow structures, such as conditional branches and loops, that depend on the randomness of inputs or other variables. Failing to handle general control-flow structures seriously restricts the applicability of analytical methods for dynamic range estimation, and makes lack-of-insight and costly profiling the only solutions for many applications. In this paper, we propose the first analytical method capable of handling general control-flow structures (especially random branches and loops) by utilizing a powerful mathematic tool, polynomial chaos expansion (PCE). Our method brings the application scope of analytical method for range estimation to general systems with control-flow structures for the first time, and it achieves high accuracy and orders of magnitude more efficiency than profiling.
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具有控制流结构系统的动态范围估计
人们已经广泛认识到,应用程序的动态范围信息可以用来减少处理器或asic的数据路径位宽,从而减少整个电路的面积、延迟和功耗。动态范围估计的分析方法有很多。然而,由于控制流结构的顽固性,目前所有可用的方法都只考虑由纯数据流结构/操作组成的系统,而一般的数字应用总是包含一些控制流结构,如条件分支和循环,这些结构依赖于输入或其他变量的随机性。不能处理一般的控制流结构严重限制了动态范围估计的分析方法的适用性,并且使缺乏洞察力和昂贵的分析成为许多应用的唯一解决方案。在本文中,我们利用一个强大的数学工具,多项式混沌展开(PCE),提出了第一种能够处理一般控制流结构(特别是随机分支和循环)的分析方法。该方法首次将解析法的应用范围扩展到具有控制流结构的一般系统中,并取得了较高的精度,效率比轮廓法提高了几个数量级。
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