A Lightweight DRDPG-Based RL DVFS for Video Rendering on CPU-GPU Integrated SoC
Bibliographic record
Abstract
Reinforcement learning (RL) based dynamic voltage and frequency scaling (DVFS) is an effective approach to balance performance and power consumption for video rendering applications. To approximate the “God’s eye view” regulation, the CPU-GPU should be fine-grained regulated and the SoC have to be fully observed by a RL-based DVFS governor. Fine-grained regulation with traditional value-based RL governor suffers action space explosion and it is impossible to have a fully observable SoC. To address these two issues, a governor based on deep recurrent deterministic gradient (DRDPG) governor is proposed. The governor is based on Deterministic Policy Gradient (DDPG) algorithm with embedded recurrent neural network (RNN). The DDPG algorithm guarantees fine-grained power regulation without action space explosion and the RNN-FC network topology mitigates the partial observability issue. Evaluated on the Nvidia Jetson NX platform, the proposed DRDPG governor achieves over 19% better regulation efficiency compared with Linux default governors and shows superior regulation efficiency to other RL-based state-of-the-arts. Implemented in a 55-nm CMOS process, the proposed governor draws merely 2.16-mA from a 1.2-V supply at 1-MHz clock occupying a silicon area of 0.075mm <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^2$</tex-math> </inline-formula> .
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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 | 0.001 | 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 | 0.001 | 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".