Design-for-adaptivity of microarchitectures
Bibliographic record
Abstract
In the last decade we have witnessed a steady trend towards functional diversification of hardware, because an application specific hardware component is a lot easier to design and optimise than a general-purpose one. Therefore, a modern microelectronics system often contains several application specific cores, each targeted for a particular function. As operating conditions issues are becoming more important, we start to see non-functional diversification in terms of performance and energy consumption; it is expected that a system can operate in a wide spectrum of environmental conditions and it should support a hierarchy of energy-saving modes. As a result, "mode-specific" processing cores are gaining popularity. The number of possible combinations of functional and nonfunctional variations of hardware components is becoming unmanageable and is leading to inefficient silicon utilisation. In this paper we explore a novel approach to hardware design which allows building computation systems capable of adjusting to operating conditions through dynamic reconfiguration. We demonstrate the approach by designing an asynchronous microprocessor core that can operate in a wide range of supply voltages and can adjust its functionality towards a specific application and operating mode. Our methodology is based on a novel model of hardware description and on self-timed design techniques.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".