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23,000-Exposures/s 360fps-Readout Software-Defined Image Sensor with Motion-Adaptive Spatially Varying Imaging Speed

2024· article· en· W4401880711 on OpenAlexaff
Roberto Rangel, Xiaonong Sun, Ayandev Barman, Rahul Gulve, Savo Bajic, Jingmin Wang, Haoxiang Wang, David B. Lindel, Kiriakos N. Kutulakos, Roman Genov

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicCCD and CMOS Imaging Sensors
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer scienceComputer visionSoftwareImage sensorArtificial intelligenceMotion (physics)

Abstract

fetched live from OpenAlex

The software-defined VGA image sensor adapts to fast changes in the scene, both locally and globally, by taking advantage of programmable-size/speed super-pixels and scalable-rate SAR ADCs, respectively. Each super-pixel includes up to 8×8 pixels exposed in a rapid sequence within one frame period, yielding a 64x maximum boost of the exposure rate over the output rate, without the corresponding increase in the ADC power. At 360fps readout, this corresponds to over 23,000 exposures/s while using only 24.5mW, and comes at the cost of moderately lower local resolution, only in the regions with the fast changes in the scene. The 54fJ-FoM SAR ADC allows for scaling down to the 30fps ‘slow’ mode, yielding an additional factor of 12x savings in the output data and camera digital power, and 30% savings in the ADC power.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.773
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.008
GPT teacher head0.203
Teacher spread0.195 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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