Instantaneous current modeling in a complex VLIW processor core
Bibliographic record
Abstract
Measuring and modeling instantaneous current consumption or current dynamics of a processor is important in embedded system designs, wireless communications, low-energy mobile computing, security of communications, and reliability. In this paper, we introduce a new instruction-level based macromodeling approach for instantaneous current consumption in a complex processor core along with new instantaneous current measurement techniques at the instruction and program level. Current consumption and voltage supply waveforms of a processor core were acquired by a sampling oscilloscope through an external interrupt-based setup. Accurate measurements of current, power and energy consumption at the instruction, block, or program level were obtained from analyzing the stored current and voltage waveforms. The current simulation methodology uses elementary functions called atomic functions to approximate the instantaneous current consumption at the instruction level. Based on these atomic functions, a simulated instantaneous current waveform at the program level was built. First, a base waveform of the current simulation was generated by the use of four basic current superposition principles. Secondly, a final waveform of the simulated current was generated from the base waveform by applying a factorial adjustment as a function of the instruction parallelism and sequencing. Step-by-step modeling procedures with numerical examples are presented. The model captured 98% of the variation of the instantaneous current for six complex applications, with an average RMS error of less than 2.2% of the average measured mean. Energy estimates obtained by the use of the simulated current waveforms were within 1.4% of the measured values. This research is important, since for the first time highly accurate instruction-based models of instantaneous current and power for complex processor cores have been developed.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".