为包含黑盒ip组件的行为描述启用能量感知设计决策

Lars Kosmann, Daniel Lorenz, A. Reimer, W. Nebel
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引用次数: 2

摘要

设计数字电路的抽象水平正在上升,因为高级合成工具正在获得认可,并可从不同的供应商。同时,对更高抽象层次上精确的能量估计的需求也在增加。但是在这些抽象层次上估计能量是一项困难的任务,因为开关容量和面积取决于在高级综合过程中做出的调度和分配决策。本文将当前的能量估计方法扩展为功率估计方法,以实现行为层面的能量感知设计。能量估计使用控制流信息来建模组件的能量和运行时,而功率估计方法通过监视外部端口行为并将其与功耗相关联来生成功率和协议状态机。对线性预测编码算法的方法进行了评估,该算法从作为黑盒ip组件提供的存储块接收其输入数据。通过使用所提出的估计方法,可以在行为层面决定该存储元件的使用是否违反给定的功率预算。能量估计的平均误差为12.55%,运行时间估计的平均误差为1.5%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enabling energy-aware design decisions for behavioural descriptions containing black-box IP-components
The abstraction level of designing digital circuits is rising since high-level synthesis tools are gaining acceptance and are available from different vendors. Simultaneously, the demand for accurate energy estimations on higher abstraction levels is increasing. But estimating energy on these abstraction levels is a difficult task since switching capacitances and area depend on scheduling and allocation decisions which are made during high-level synthesis. In this paper a current energy estimation methodology is extended by a power estimation approach to enable energy-aware design designs on behavioural level. The energy estimation uses control-flow information to model energy and runtime of a component while the power estimation approach generates power and protocol state machines by monitoring external port behaviour and putting it in relation to power dissipation. The methodology is evaluated for a linear predictive coding algorithm receiving its input data from a memory block which is provided as a black-box IP-component. By using the presented estimation methodology, it can be decided at behavioural level whether the usage of this memory element violates a given power budget. The average estimation error for energy is 12.55% while runtime can be estimated with an error of 1.5%.
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