2 research outputs found
FPGA ARCHITECTURE FOR 2D DISCRETE FOURIER TRANSFORM BASED ON 2D DECOMPOSITION FOR LARGE-SIZED DATA
ABSTRACT Applications based on Discrete Fourier Transforms (DFT) are extensively used in various areas of signal and digital image processing. Of particular interest is the two-dimensional (2D) DFT which is more computation-and bandwidth-intensive than the one-dimensional (ID) DFT. Traditionally, a 2D DFT is computed using Row-Column (RC) decomposition, where ID DFTs are computed along the rows followed by ID DFTs along the columns. Both application specific and reconfigurable hardware have been used for high-performance implementations of 2D DFT. However, architectures based on RC decomposition are not efficient for large input size data due to memory bandwidth constraints. In this paper, we propose an efficient architecture to implement the 2D DFT for largesized input data based on a novel 2D decomposition algorithm. This architecture achieves very high throughput by exploiting the inherent parallelism due to the algorithm decomposition and by utilizing the row-wise burst access pattern of the external memory. A high throughput memory interface has been designed to enable maximum utilization of the memory bandwidth. In addition, an automatic system generator is provided for mapping this architecture onto a reconfigurable platform of Xilinx Virtex5 devices. For a 2K x 2K input size, the proposed architecture is 1.96x times faster than RC decomposition based implementation under the same memory constraints, and also outperforms other existing implementations
Near Memory Acceleration on High Resolution Radio Astronomy Imaging
Modern radio telescopes like the Square Kilometer Array (SKA) will need to
process in real-time exabytes of radio-astronomical signals to construct a
high-resolution map of the sky. Near-Memory Computing (NMC) could alleviate the
performance bottlenecks due to frequent memory accesses in a state-of-the-art
radio-astronomy imaging algorithm. In this paper, we show that a sub-module
performing a two-dimensional fast Fourier transform (2D FFT) is memory bound
using CPI breakdown analysis on IBM Power9. Then, we present an NMC approach on
FPGA for 2D FFT that outperforms a CPU by up to a factor of 120x and performs
comparably to a high-end GPU, while using less bandwidth and memory