Embedded power-aware cycle by cycle variable speed processor
Bibliographic record
Abstract
A variable speed processor (VSP) that can adjust its clock period at each cycle, according to the instruction flow in a pipelined program, is presented. This allows performance enhancement and energy consumption reduction, which is an important consideration for the next generation of embedded processor designs. With little change to the standard synchronous design, speed can be enhanced without increasing energy or speed can be maintained with energy savings. The VSP concept is validated by coupling a Nios® processor with a variable period clock synthesiser (VPCS). No modifications to the core other than extracting internal signals from the pipeline are needed to control the VPCS. The VPCS cleanly switches between period lengths at each cycle, over a wide range of possible lengths and with any resolution depending on available clock phases. One VPCS design, in CMOS 0.18 µm, consumes less than 10 µW/MHz and is able to instantly switch inside the 4–250 MHz range. The VSP design is implemented with the Altera® Embedded System platform, in its Stratix® FPGA. With the proposed method, the dynamic energy consumed per program loop is reduced by 14%, while the processing time is reduced by 3.6% compared to the original standard Nios® processor running the same program at its maximum frequency (133 MHz).
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".