A modular architecture for hybrid VLSI neural networks and its application in a smart photosensor
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We describe a modular architecture for the VLSI implementation of multilayer neural networks using a universal hybrid building block. Based on this approach, a programmable smart photosensor is designed which is in fact a VLSI realization of a multilayer feedforward neural network with an integrated photoreceptor array using 1.2 /spl mu/m CMOS technology. Each universal building block in this architecture comprises a multiplying DAC synapse, a portion of a nonlinear distributed neuron and compact digital registers for programming and storing a synaptic weight. The proposed modular neural network architecture features design simplicity and scalability, area efficiency, reduced interconnection problems and increased robustness. Based on this architecture and using cell-level optimization, the synaptic density in this version of the neural-based smart sensor has been increased by a factor of two. This has lead to an increase in the area available for a larger and higher resolution optical input array.
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.
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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it