FPGA-based Matched Filter Group Optimisation for SKA Pulsar Search Engine

TL;DR

This paper presents FPGA-based optimizations for matched filtering in the SKA pulsar search engine, analyzing design strategies for different filter sizes and comparing performance with GPUs.

Contribution

It introduces optimized FPGA designs for matched filtering, employing time-domain and Fourier-domain approaches, tailored for SKA pulsar search requirements.

Findings

Time-domain optimization uses LPT rule for filter distribution.

Fourier-domain analysis links memory use to speedup.

Arria 10 FPGA achieves comparable performance per watt to high-end GPUs.

Abstract

Pulsar search is one of the main tasks for the Square Kilometre Array (SKA), implemented in the central signal processor (CSP) sub-element. As most the characteristics of undiscovered pulsars are unknown by definition, exhaustive searches over a multi-dimensional parameter space are employed. One main compute-intensive task of the pulsar search modules in the CPS is the matched filter group, which convolves the input signals with a group of large FIR filters. High-performance designs on FPGAs have been proposed that can process multiple large filters efficiently. But given that in many applications, including the here targeted pulsar search, FIR filters have many different sizes, there is further potential for optimisation. This paper investigates the optimisation of matched filtering designs. While the results are tranferable to other domains, we are motivated by the needs of the SKA pulsar search engine. The influence of changing number of filters and the difference in sizes is analysed. The generic design in time-domain (TD) is optimised by employing the longest processing time (LPT) first rule to distribute filter templates across filter processing pipelines. For the Fourier-domain (FD), the relationship between the required off-chip memory space and speedup over the generic design is investigated. To put the results into relation with with GPU design, we compared with a well-optimised design for top-end GPUs (NVIDIA Tesal P100). While a mid-range Intel Arria 10 is up to 7.5x slower than the P100, the performance per watt is slightly better on the Arria 10.