Use of "virtual" (simulated) hardware devices in microprocessor laboratories and tutorials
Bibliographic record
Abstract
It is a common problem in industry that the development of software does not go hand-in-hand with the development of the hardware that the software is intended to control. A similar situation can occur in the undergraduate laboratory. Here a student, having designed the software component of a project, can't gain access to the necessary hardware to prepare for or complete a laboratory because of schedule/security difficulties. Over the past year we have overcome this problem by using "virtual" hardware, where device operation is simulated in software. We have generalized the approach so that the virtual devices can be used in conjunction with microprocessor simulator software and with actual evaluation boards for both RISC and CISC systems. We are in the preliminary development stages of a new HTML Web page approach where we control, rather than just launch, these commercial simulation packages. Such an approach would provide a controlled, interactive, tutorial environment for students taking microprocessor courses. There are further industrial and academic advantages of such an approach which can help to overcome the initial learning curve for the tools. We discuss the basics of developing "virtual" devices for use with the Windows based development environment provided with Software Development Systems 68 K and PowerPC free sample kits. These devices can then be ported to the Motorola M68332EVK and Advanced Micro Devices' SA29200 microprocessor evaluation boards to provide actual hardware experience.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".