Integrated photonic-electronic platform for real-time analog data processing in LiDARs
Bibliographic record
Abstract
Light, detection and ranging (LiDAR), has been emerging as a powerful tool for applications with accurate and reliable perception requirements, e.g., autonomous driving which needs a combination of long-range and high spatial resolution together with a real-time performance. Processing the raw LiDAR data, which is a large-dimensional unstructured 3D point cloud, is computationally costly due to the nature of the algorithms used for processing the point clouds. In particular, the neural networks employed for LiDAR data processing comprise several layers, for each of which multiplications of matrices with large sizes need to be performed. In this case, graphics processing units (GPUs) cannot be used as real-time standalone devices for hardware acceleration because they have high execution time due to their dependency on a central processing unit (CPU) for data offloading and scheduling the execution of the algorithms used to process point clouds. To address the aforementioned challenges, we propose an efficient co-design of an analog neural network (ANN) and a hybrid CMOS-Photonics platform for LiDAR systems. The proposed architecture exploits the high bandwidth and low latency of optical computation to significantly improve the computational efficiency. In particular, in our proposed architecture, a CMOS control chip integrated with a photonic broadcast-and-weight architecture is interfaced with LiDAR to perform real-time data processing and high-dimensional matrix multiplications. Moreover, by processing the raw LiDAR data in the analog domain, the proposed hybrid electro-optic computing platform minimizes the number of data converters in LiDAR systems.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".