Exploring the Versal AI Engines for Signal Processing in Radio Astronomy

Nowadays, heterogeneous architectures are widely used to overcome the ongoing demand for increased computing performance at the edge, such as the pre-processing of raw antenna data in radio telescope systems. The Versal Adaptive SoC is a novel heterogeneous architecture that includes Programmable Logic (PL), a Processing System, and Artificial Intelligence Engines (AIEs) interconnected by a programmable Network-on-Chip. In this work, we explore the AIEs to evaluate their capabilities for real-time signal processing in radio telescope systems. We focus on the implementation of a Polyphase Filter Bank (PFB), which is a representative signal processing operation that consists of Finite Impulse Response (FIR) filters and the Fast-Fourier Transform (FFT) algorithm. We analyzed the performance of the AIEs with regard to the requirements of LOFAR, the world's largest low-frequency radio telescope. By means of the roofline model, we reveal that the AIEs theoretically meet the LOFAR computational requirements, but the FIR vendor-provided library implementation did not reach the required performance. Therefore, we explore several optimization strategies for the FIR implementation on the AIEs and analyze the communication options between the PL and the AIE array. Finally, we have developed an efficient PFB implementation that requires only 12 AIEs. A prototype on a VC1902 device achieves a throughput of 437 MSPS, which is more than sufficient for a single antenna polarization of the LOFAR system. This work is open source and publicly available at: https://git.astron.nl/rd/acap.