Current dynamics-based macro-model for power simulation in a complex VLIW DSP processor
Bibliographic record
Abstract
A methodology and a macro-modelling approach are presented for analysing low-level current dynamics at the instruction and program level for a complex VLIW DSP processor core. An instruction-level macro-model, whose input parameters can be extracted from the DSP core's assembly level program, is introduced for power modelling. For the first time, dynamic power models of algorithms are introduced and verified with real power measurements of a DSP processor core in a VLSI chip. Results from both cryptographic and bubble sort applications show that dynamic power can be modelled with an average error in energy estimation ranging from 0.3% to 9.7%. The instruction-level macro-model of power also supports different clock frequencies and compressed algorithmic traces, important for security aware compilers. In general, the research is important for analysing and modelling the impact of software on power, the design of embedded cryptographic VLSI systems that are safe from power attacks, and for reliable design by detecting the peak current values generated by the software application.
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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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".