MIPSfpga: using a commercial MIPS soft‐core in computer architecture education
Bibliographic record
Abstract
In this study, the authors introduce MIPSfpga and its accompanying set of learning materials. MIPSfpga is a teaching infrastructure that offers access to the non‐obfuscated Register‐Transfer Level (RTL) source code of the MIPS microAptiv UP processor. The core is made available by Imagination Technologies for academic use and is targeted to a field‐programmable gate array (FPGA), making it ideal for both the classroom and research. The supporting materials and labs focus on hands‐on learning that emphasises computer architecture, system on chip (SoC) design and hardware–software codesign. Among other things, students learn to set up the MIPS soft‐core processor on an FPGA, run and debug programs on the core in simulation and in hardware, add new peripherals to the system, understand the microarchitecture and extend it to support new features, experiment with different cache sizes and content management policies, add new instructions using the CorExtend interface available in MIPS processors, and understand SoCs in embedded systems and how they are designed and built up in layers to run complex software such as Linux.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.010 |
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".