高性能ALU低功耗、高效率架构的设计与实现

U. Penchalaiah, V. S. Kumar
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摘要

数字信号处理器(DSP)在几乎所有民用和军用信号处理应用中无处不在,包括关键任务环境,如核反应堆,过程控制等。算术和逻辑单元(ALU)是任何数字信号处理器的核心,在实现所需的参数基准和数字信号处理器的整体效率和鲁棒性方面起着关键和决定性的作用。最先进的研究已经成功地满足了关键的乘法累积(MAC)参数的性能要求,如降低功耗、小电子占用空间和减少延迟以及相关的设计复杂性。在ALU的架构设计中,其组成模块即截断乘法器和半和进位生成-和进位生成(HSCG-SCG)加法器的合理放置以及加法器和乘法器电路的选择是决定ALU整体性能的核心决策。为了克服这个缺点并进一步提高性能,本文提出了一种新的平方根进位选择加法器(CSLA)架构,使用半和生成(HSG)、半进位生成(HCG)、全和生成(FSG)和全进位生成(FCG)块。提出的设计包含n位架构,并考虑了8位,16位和32位组合的比较结果。所有设计都在赛灵思ISE环境中实现,结果表明,与最先进的方法相比,该方法具有更好的面积、功耗和延迟性能。
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Design and Implementation of Low Power and Area Efficient Architecture for High Performance ALU
Digital Signal Processors (DSP) have a ubiquitous presence in almost all civil and military signal processing applications, including mission critical environments like nuclear reactors, process control etc. Arithmetic and Logic units (ALU), being the heart of any digital signal processor, play critical and decisive roles in achieving the required parameter benchmarks and the overall efficiency and robustness of the digital signal processor. State of the art research has shown successful traction with the performance requirements of critical Multiply-Accumulate (MAC) parameters, like reduced power consumption, small electronic real estate footprint and reduction in delay with the associated design complexity. Judicious placement of its building blocks, namely, the truncated multiplier and half-sum carry generation-sum carry generation (HSCG-SCG) adder in the architectural design of ALU and the type of adder and multiplier circuits selected are the core decisions that decide the overall performance of the ALU. To overcome the drawback and to improve the performance further, this work proposes a new architecture for the square root (SQRT) carry select adder (CSLA) using half-sum generation (HSG), half-carry generation (HCG), full-sum generation (FSG) and full-carry generation (FCG) blocks. The proposed design contains N-bit architecture, and comparative results are considered for 8-bit, 16-bit and 32-bit combinations. All the designs are implemented in the Xilinx ISE environment and the results show that better area, power, and delay performance compared to the state of art methods.
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