The XTREM power and performance simulator for the Intel XScale core
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
Managing power concerns in microprocessors has become a pressing research problem across the domains of computer architecture, CAD, and compilers. As a result, several parameterized cycle-level power simulators have been introduced. While these simulators can be quite useful for microarchitectural studies, their generality limits how accurate they can be for any one chip family. Furthermore, their hardware focus means that they do not explicitly enable studying the interaction of different software layers, such as Java applications and their underlying runtime system software. This paper describes and evaluates XTREM, a power-simulation tool tailored for the Intel XScale microarchitecture. In building XTREM, our goals were to develop a microarchitecture simulator that, while still offering size parameterizations for cache and other structures, more accurately reflected a realistic processor pipeline. We present a detailed set of validations based on multimeter power measurements and hardware performance counter sampling. XTREM exhibits an average performance error of only 6.5% and an even smaller average power error: 4%. The paper goes on to present an application study enabled by the simulator. Namely, we use XTREM to produce an energy consumption breakdown for Java CDC and CLDC applications. Our simulator measurements indicate that a large percentage of the total energy consumption (up to 35%) is devoted to the virtual machine's support functions.
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 | 0.002 | 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 | 0.002 | 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