AnalogNet: Convolutional Neural Network Inference on Analog Focal Plane\n Sensor Processors
Bibliographic record
Abstract
We present a high-speed, energy-efficient Convolutional Neural Network (CNN)\narchitecture utilising the capabilities of a unique class of devices known as\nanalog Focal Plane Sensor Processors (FPSP), in which the sensor and the\nprocessor are embedded together on the same silicon chip. Unlike traditional\nvision systems, where the sensor array sends collected data to a separate\nprocessor for processing, FPSPs allow data to be processed on the imaging\ndevice itself. This unique architecture enables ultra-fast image processing and\nhigh energy efficiency, at the expense of limited processing resources and\napproximate computations. In this work, we show how to convert standard CNNs to\nFPSP code, and demonstrate a method of training networks to increase their\nrobustness to analog computation errors. Our proposed architecture, coined\nAnalogNet, reaches a testing accuracy of 96.9% on the MNIST handwritten digits\nrecognition task, at a speed of 2260 FPS, for a cost of 0.7 mJ per frame.\n
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".