The Implementation of a Real-Time Polyphase Filter

TL;DR

This paper evaluates different computational accelerators for real-time data processing using the polyphase filter algorithm, finding GPUs to be the most suitable due to their superior performance and power efficiency.

Contribution

It provides a comparative analysis of accelerators for real-time polyphase filtering, highlighting GPUs as the optimal choice for high data rate applications.

Findings

GPUs outperform other accelerators in performance.

GPUs consume less power for the task.

Suitable for 6.5GB/s data rate processing.

Abstract

In this article we study the suitability of dierent computational accelerators for the task of real-time data processing. The algorithm used for comparison is the polyphase filter, a standard tool in signal processing and a well established algorithm. We measure performance in FLOPs and execution time, which is a critical factor for real-time systems. For our real-time studies we have chosen a data rate of 6.5GB/s, which is the estimated data rate for a single channel on the SKAs Low Frequency Aperture Array. Our findings how that GPUs are the most likely candidate for real-time data processing. GPUs are better in both performance and power consumption.