Bibliographic record
Abstract
Mobile systems-on-chips (SoCs) have become ubiquitous computing platforms, and, in recent years, they have become increasingly heterogeneous and complex. A typical SoC includes CPUs, graphics processor units (GPUs), image processors, video encoders/decoders, AI engines, digital signal processors (DSPs) and 2D engines among others [33, 70, 71]. One of the most significant SoC units in terms of both off-chip memory bandwidth and SoC die area is the GPU. In this paper, we present Emerald, a simulator that builds on existing tools to provide a unified model for graphics and GPGPU applications. Emerald enables OpenGL (v4.5) and OpenGL ES (v3.2) shaders to run on GPGPU-Sim's timing model and is integrated with gem5 and Android to simulate full SoCs. Emerald thus provides a platform for studying system-level SoC interactions while including the impact of graphics.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.342 | 0.318 |
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".