A Methodology to Evaluate the Energy Efficiency of Application Specific Processors
Why this work is in the frame
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Bibliographic record
Abstract
This paper proposes an FPGA based methodology to assess the energy efficiency of application specific processors (ASIPs). This methodology is applied to a video processing algorithm, the motion compensated frame rate conversion (MC-FRC). Previous work has shown that designing a specific instruction set can enhance the performance with a speed-up of more than 80 fold. The purpose of this work is to quantify the energy efficiency of the resulting accelerated processor. This efficiency is evaluated by estimating the power and energy consumption of the processor and of the ASIP when running the algorithm. The results obtained show that the ASIP is more energy efficient than the standard processor by a factor of at least 40. This paper describes the methodology used to compute the power and energy consumption and explains the results through a more detailed analysis of the power and energy consumption.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it