A VHDL library of IP cores for power drive and motion control applications
Bibliographic record
Abstract
This paper presents the design and implementation of a library of modules, called IP ("Intellectual Property") cores, to be used in the design of power drive and motion control applications. The library was coded in VHDL for modularity and portability. The IP cores were validated in a XC4010XL FPGA and XESS XS40 prototyping board. Very frequently, power drive and motion control applications are implemented using a DSP or microcontroller and the algorithms are written in assembly or in a high level language such as C. By using VHDL to describe the circuit, we implement the algorithms directly in hardware, instead of writing a sequential program, which allows the development of high performance circuits but without increasing the cost of the product. Plus, it keeps the same advantages that made software design flow so popular: that is, it supports parameter-driven modules, top-down and bottom-up design flow and reusable coding. Besides, we may use a unified design framework to specify, model, simulate and implement the project. All tasks are realized in the same design environment and using the same tools, which is not supported in a software design flow.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.009 |
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".