The What's Next Intermittent Computing Architecture
Bibliographic record
Abstract
Energy-harvesting devices operate under extremely tight energy constraints. Ensuring forward progress under frequent power outages is paramount. Applications running on these devices are typically amenable to approximation, offering new opportunities to provide better forward progress between power outages. We propose What's Next (WN), a set of anytime approximation techniques for energy harvesting: subword pipelining, subword vectorization and skim points. Skim points fundamentally decouple the checkpoint location from the recovery location upon a power outage. Ultimately, WN transforms processing on energy-harvesting devices from all-or-nothing to as-is computing. We enable an approximate (yet acceptable) result sooner and proceed to the next task when power is restored rather than resume processing from a checkpoint to yield the perfect output. WN yields speedups of 2.26x and 3.02x on non-volatile and checkpoint-based volatile processors, while still producing high-quality outputs.
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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 | 0.000 | 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 | 0.000 | 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".