FlipBit: Approximate Flash Memory for IoT Devices
Bibliographic record
Abstract
IoT devices commonly use flash memory for both data and code storage. Flash memory consumes a significant portion of the overall energy of such devices. This is problematic because IoT devices are energy constrained due to their reliance on batteries or energy harvesting. To save energy, we leverage a unique property of flash memory; write operations take unequal amounts of energy depending on if we are flipping a 1 → 0 versus a 0 → 1. We exploit this asymmetry to reduce energy consumption with FLIPBIT, a hardware-software approximation approach that limits costly 0→1 transitions in flash. Instead of performing an exact write, we write an approximated value that avoids any costly 0→1 bit flips. Using FLIPBIT, we reduce the mean energy used by flash by 68% on video streaming applications while maintaining 42 dB PSNR. On machine learning models, we reduce energy by an average of 39% and up to 71% with only a 1% accuracy loss. Additionally, by reducing the number of program-erase cycles, we increase the flash lifetime by 68%.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".