Compiling CNNs with Cain: focal-plane processing for robot navigation
Bibliographic record
Abstract
Abstract Focal-plane Sensor-processors (FPSPs) are a camera technology that enables low power, high frame rate computation in the image sensor itself, making them suitable for edge computation. To fit into the sensor array, FPSPs are highly resource-constrained, with limited instruction set and few registers - which makes developing complex algorithms difficult. In this work, we present Cain, a compiler for convolutional filters that targets SCAMP-5, a general-purpose FPSP. Cain generates code to evaluate multiple convolutional kernels at the same time. It generates code that avoids the need for hardware multipliers, while orchestrating the exploitation of common sub-terms—leading to a large reduction in instruction count compared to both straightforward and prior optimized approaches. We demonstrate the capability enabled by Cain on SCAMP-5 with robotic navigation for near-sensor high-speed and low-power computation, by using Cain to implement a neural network on the focal plane.
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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) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".