New Memory Architecture for Rolling Shutter Wide Dynamic Range CMOS Imagers
Bibliographic record
Abstract
In this work, the concept of reusing a memory location to significantly reduce the overall memory size for storing wide dynamic range (WDR) information in rolling shutter active pixel sensors (APSs) is discussed. At the high light level, WDR is achieved via multiple-resets and real time feedback, allowing a pixel to independently set its integration period as per its ambient light level. Traditionally these WDR bits are stored in a dedicated memory location for every pixel. We propose a new memory architecture which, in principal, is similar to time division multiplexing, such that it achieves memory size reduction by sharing a single memory location among a number of pixels as a function of time. The proposed architecture is ideally suited for rolling shutter APS, where each row is processed sequentially in time. Compared to a commonly used memory design, the proposed architecture becomes increasingly efficient as the pixel count increases, resulting in momentous savings in memory chip area and leakage power consumption. For a pixel array of 128$\ast$128, only 14.2% of the commonly used memory bits are required, when using 7 WDR bits per pixel. This requirement reduces to 8.3% of the commonly used memory bits for a pixel array size of 4096$\ast$4096, rendering the purposed architecture particularly efficient for larger arrays. The savings in leakage power will track the corresponding savings in memory size and area especially for newer technologies. The purposed concept has been verified in design and simulation for a 128$\ast$128 pixel array, fabricated in 180 nm technology.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".