Measuring the dynamic energy efficiency of FPGAs over processors
Bibliographic record
Abstract
This work investigates the dynamic energy efficiency of the parallel execution model of an FPGA and the sequential execution model of a processor, for latency-insensitive applications. We create the temporal implementations (sequential instructions) of the MCNC benchmarks to be executed on a processor that employs a 4LUT as its functional unit. This processor is ~716 times inefficient for dynamic energy than a 4LUT FPGA, mainly due to the large amount of memory (instruction/data) that is required to encode the 4LUT based instructions. The size of the memory (instruction/data) can be reduced by increasing the data-path width and the logic complexity of the ASIC-based functional units of the processor. Particularly, at 64-bit data-path width and when the (instruction/data) memory sizes are reduced to less than ~9% of their corresponding 4LUT-based instructions, the processor with ASIC-based complex functional unit can achieve higher dynamic energy efficiency than the FPGA for MCNC benchmarks.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.002 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".