Cocotb-Pynq: Co-Simulating Python+RTL Applications Targeting Pynq Platforms with Cocotb
Bibliographic record
Abstract
The AMD Pynq ecosystem fails to provide a seamless way to easily validate functional correctness of RTL designs when part of the application logic runs in Python on the ARM (or x86) host CPU. Application developers must wait for the entire FPGA bitstream generation flow and deploy their code to the FPGA before they confirm the correctness of the Python host code working with the RTL design implemented on the FPGA. In contrast, Cocotb offers a Pythonic framework to test and simulate RTL designs in a variety of cycle-accurate simulators, but lacks easy integration with the Pynq ecosystem. In this paper, we propose Cocotb-Pynq, a framework for co-simulating Python ARM (or x86) host code and RTL/Verilog programs in a single environment. This eliminates the need for bitstream generation prior to co-simulation of Python and RTL components and significantly speeds up design iterations. We rewrite key components of the Pynq ecosystem to be cocotb-compatible and offer drop-in solutions for Pynq APIs in Cocotb. Specifically, we rewrite the MMIO and AXI DMA blocks using the Python asyncio library to be compatible with Cocotb emulation. We evaluate our framework on a suite of benchmark programs and quantify their performance. In contrast to bitstream generation times of 10 minutes needed for Pynq devices such as PynqZ1 for our small benchmarks with modest frequency targets, a Cocotb-Pynq co-simulation takes 1-2 minutes of runtime even for large designs using the entire chip. The framework will be open-sourced and made available for community contributions and evolution.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".