Cocotb-Pynq: Co-Simulating Python+RTL Applications Targeting Pynq Platforms with Cocotb
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it