FPGA-Based Embedded System Implementation of Audio Signal Alignment

FPGAs are considered a valuable solution for embedded system applications thanks to their performance, energy efficiency and capability to face system failures. However, the number of available applications is limited due to the learning curve needed to customize FPGA-based accelerators. As proof of...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:2019 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) s. 132 - 139
Hlavní autoři: Stornaiuolo, Luca, Perini, Massimo, Santambrogio, Marco D., Sciuto, Donatella
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.05.2019
Témata:
alignement
audio
Embedded systems
Feature extraction
Field programmable gate arrays
FPGA
Libraries
NumPy
PYNQ
Python
signal
SoC
Synchronization
Zynq
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:FPGAs are considered a valuable solution for embedded system applications thanks to their performance, energy efficiency and capability to face system failures. However, the number of available applications is limited due to the learning curve needed to customize FPGA-based accelerators. As proof of this, Xilinx recently released PYNQ, a platform for Zynq SoC that relies on Python and overlays to ease the integration of functionalities of the programmable logic into applications. In this work, we build upon this framework to implement an optimized embedded design for audio alignment and we integrated it in the Python applications workflow. In particular, we provide a custom accelerator designed for PYNQ and the software interface to transparently exploit the programmable logic from the Python code runs on the embedded CPU. We then compare the executions on two different devices: the PYNQ-Z1 and the Raspberry Pi 3. Our FPGA accelerated implementation is able to reach a speedup of 12.4x with respect to the PYNQ-Z1, when only the CPU is used, and a speedup of 5.5x with respect to the Raspberry Pi 3 version.
DOI:10.1109/IPDPSW.2019.00031