Energy simulation of embedded XScale systems with XEEMU
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Energy efficiency is key in embedded system design. Understanding the complex issue of software power consumption in early design phases is of extreme importance to make the right design decisions. Here, not only the CPU but also the external memory plays a very important role. Power simulators offer flexibility and allow a detailed view on the sources of power consumption. However, many simulators lack accuracy and focus only on the CPU core without considering the memory subsystem. In this paper, we present XEEMU, a fast, cycle-accurate simulator, which aims at accurately simulating the power consumption of an XScale-based system including its memory subsystem. It has been validated using measurements on real hardware and shows a high accuracy for runtime, instantaneous power, and total energy consumption estimation. The average error is as low as 3.0% and 1.6% for runtime and CPU energy consumption estimation, respectively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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