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Efficient VLSI Architecture for Decimation-in-Time Fast Fourier Transform of Real-Valued Data

机译:高效的VLSI架构,用于实时抽取实值数据的快速傅里叶变换

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The decimation-in-time (DIT) fast Fourier transform (FFT) very often has advantage over the decimation-in-frequency (DIF) FFT for most real-valued applications, like speech/image/video processing, biomedical signal processing, and time-series analysis, etc., since it does not require any output reordering. Besides, the DIT FFT butterfly involves less computation time than its DIF counterpart. In this paper, we present an efficient architecture for the radix-2 DIT real-valued FFT (RFFT). We present here the necessary mathematical formulation for removing the redundancies in the radix-2 DIT RFFT, and present a formulation to regularize its flow graph to facilitate folded computation with a simple control unit. We propose here a register-based storage design which involves significantly less area at the cost of a little higher latency compared with the conventional RAM-based storage. The address generation for folded in-place DIT RFFT computation with register-based storage is challenging since both read and write operations are performed in the same clock cycle at different locations. Therefore, we present here a simple formulation of address generation for the proposed radix-2 DIT RFFT structure. The proposed structure involves 61% less area and 40% less power consumption than those of , on average, for FFT sizes 16, 32, 64, and 128. It involves 70% less area-delay product and 57% less energy per sample than those of the other, on average, for the same FFT sizes.
机译:对于大多数实值应用(例如语音/图像/视频处理,生物医学信号处理和实时处理),实时抽取(DIT)快速傅里叶变换(FFT)常常比频率抽取(DIF)FFT具有优势。时间序列分析等,因为它不需要任何输出重新排序。此外,DIT FFT蝶形比其DIF对应蝶形包含更少的计算时间。在本文中,我们为基数2 DIT实值FFT(RFFT)提供了一种有效的体系结构。在这里,我们提出了必要的数学公式,用于消除基数为2的DIT RFFT中的冗余,并提出了一种规范化其流程图的公式,以利于使用简单的控制单元进行折叠计算。我们在这里提出了一种基于寄存器的存储设计,与传统的基于RAM的存储相比,该设计涉及的面积大大减少,但延迟时间却稍高一些。具有基于寄存器的存储的折叠式就地DIT RFFT计算的地址生成具有挑战性,因为读取和写入操作都在同一时钟周期中的不同位置执行。因此,我们在这里为提出的基数2 DIT RFFT结构提供一种简单的地址生成公式。与FFT大小16、32、64和128相比,所建议的结构平均面积要小61%,功耗要低40%。与每个样本相比,它的面积延迟乘积少70%,能量减少57%对于相同的FFT大小,平均而言是其他方法的结果。

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