Studying the Impact of CPU and Memory Controller Frequencies on Power Consumption of the Jetson TX1
Bibliographic record
Abstract
Nowadays, heterogeneous unified memory architecture platforms are becoming increasingly common. These platforms incorporate several co-processors on a single chip with a shared physical memory. The use cases for such platforms can vary dramatically. On the one hand, they can be used in the context of Edge computing, which cannot tolerate high latency and has strict energy/power constraints. On the other hand, motivated by their growing computing capabilities, and their energy-efficiency, many have considered replacing traditional bulky servers with these platforms to deliver the same computing power but with lower energy budget. This study is an exploratory step to understand the trade-off between power consumption, processing time, and throughput on a low-power heterogeneous platform. We focus on data stream processing workloads by characterizing several common computing kernels found in computer vision algorithms. Our preliminary experiments on NVIDIA Jetson TX1 show that it is possible reduce power consumption by up to 12%.
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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 | 0.000 | 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 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".