Memory Conscious Machine Learning Method to Extract Time-of-Flight Data From Flash LiDARs
Bibliographic record
Abstract
This letter presents a memory-conscious architecture for single-photon-avalanche-diode (SPAD) light detection and ranging (LiDAR) systems to extract time-of-flight (ToF) information by learning the distribution of incoming photons' ToF to reduce the I/O bandwidth required by flash LiDAR digitizers. Such LiDAR systems can be sensitive to single photons, which makes them an excellent choice for both short- and long-range applications. However, counting and processing every photon means that the sensor front-end generates a massive amount of data to be processed in each frame. The proposed architecture will tackle this challenge by learning the incoming photons' distribution on-chip using mini-batches of time-stamped data from time-to-digital converters. Hence, only learned parameters need to be transferred off-chip instead of the raw data to re-create the histogram. We demonstrate the feasibility of this model in a Monte Carlo simulation.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".