Real-Time Event Handling and Preemptive Hardware RTOS Scheduling on a Custom CPU Implementation
Bibliographic record
Abstract
The rapid evolution of field-programmable gate array (FPGA) devices has strongly influenced both the design methodology and development tools. This article describes an original implementation based on a hardware structure used for static and dynamic task scheduling. The proposed custom processor has hardware-implemented RTOS (HW-RTOS) features and is verified using an FPGA circuit. The solution replaces the classical stack save concept with a resource remapping mechanism that enables a new task to be executed starting with the next processor cycle. The proposed hardware scheduler enables unified management of events and interrupts, by implementing a method of attaching interrupts to tasks while ensuring the requirements of real-time systems. The robustness and performance of the proposed platform are guaranteed by the context switch operations, presence of the intertask synchronization and communication mechanisms, and by the efficient use of the multiplexed resources. Instead of saving and restoring general-purpose registers into the memory, the latency is removed by directly commuting the datapath task resources.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".