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VeriPy -- A New Python-Based Approach for SDR Pipelined/Unrolled Hardware Accelerator Generation

arXiv 2512.00006 ↗First posted 9 Oct 2025cs.AR cs.CLcited by 0

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Abstract (arXiv)

Software-defined radio (SDR) plays an important role in the communication field by providing a flexible and customized communication system for different purposes according to the needs. To enhance the performance of SDR applications, hardware accelerators have been widely deployed in recent years. In facing this obstacle, a necessity arises for a high-level synthesis (HLS) tool specifically designed for communication engineers without detailed hardware knowledge. To lower the barrier between SDR engineers and hardware development, this work proposed a Python-based HLS tool, VeriPy, which can generate both mainstream architecture for hardware accelerators in Verilog specifically for SDR designs including unrolled design and pipelined design, requiring no detailed digital hardware knowledge or Hardware Description Languages (HDL). Furthermore, VeriPy supports automatic testbench generation with random input stimulus, an extensible hardware library, performance and resource estimation, and offers strong optimisation potential at both the algorithmic and digital hardware levels. The generated hardware design by VeriPy can achieve up to 70% faster operating frequency compared to pragma-optimised Vivado HLS designs with a reasonably higher resource con-sumption while delivering comparable performance and resource consumption to hand-coded implementations. Regarding code complexity, VeriPy requires no pragmas, completely eliminating the need for low-level hardware knowledge. For straightforward algorithms, the input code length remains comparable to that of Vivado HLS.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 3 of its 28 references that are in the map. First posted on arXiv 9 Oct 2025.

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References · 3

  1. 2018Software-defined Radios: Architecture, State-of-the-art, and Challengescited by 3
  2. 2024RapidStream IR: Infrastructure for FPGA High-Level Physical Synthesiscited by 2
  3. 2021Machine Learning for the Control and Monitoring of Electric Machine Drives: Advances and Trendscited by 1

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