FPGA-based Acceleration of FT Convolution for Pulsar Search Using OpenCL

Abstract

The Square Kilometre Array (SKA) project will be the world largest radio telescope array. With its large number of antennas, the number of signals that need to be processed is dramatic. One important element of the SKA's Central Signal Processor package is pulsar search. This paper focuses on the FPGA-based acceleration of the Frequency-Domain Acceleration Search module, which is a part of SKA pulsar search engine. In this module, the frequency-domain input signals have to be processed by 85 Finite Impulse response (FIR) filters within a short period of limitation and for thousands of input arrays. Because of the large scale of the input length and FIR filter size, even high-end FPGA devices cannot parallelise the task completely. We start by investigating both time-domain FIR filter (TDFIR) and frequency-domain FIR filter (FDFIR) to tackle this task. We applied the overlap-add algorithm to split the coefficient array of TDFIR and the overlap-save algorithm to split the input signals of FDFIR. To achieve fast prototyping design, we employed OpenCL, which is a high-level FPGA development technique. The performance and power consumption are evaluated using multiple FPGA devices simultaneously and compared with GPU results, which is achieved by porting FPGA-based OpenCL kernels. The experimental evaluation shows that the FDFIR solution is very competitive in terms of performance, with a clear energy consumption advantage over the GPU solution.

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“…A number of recent works relies on the OpenCL language for FPGAs to deploy advanced services such as Convolutional Neural Networks (CNNs), Finite Impulsive Response (FIR) filters or image processing accelerations. In [14], the authors provide an exhaustive analysis about the Time-Domain Finite Impulsive Response (TDFIRs) and Frequency-Domain Finite Impulsive Response (FDFIRs) implemented in the FPGA by means of OpenCL. In particular, they focus on the performance of several FIR implementations on the FPGA as well as performance comparison between FPGA and GPGPU.…”