Kerneltron: support vector "machine" in silicon
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
Detection of complex objects in streaming video poses two fundamental challenges: training from sparse data with proper generalization across variations in the object class and the environment; and the computational power required of the trained classifier running real-time. The Kerneltron supports the generalization performance of a support vector machine (SVM) and offers the bandwidth and efficiency of a massively parallel architecture. The mixed-signal very large-scale integration (VLSI) processor is dedicated to the most intensive of SVM operations: evaluating a kernel over large numbers of vectors in high dimensions. At the core of the Kerneltron is an internally analog, fine-grain computational array performing externally digital inner-products between an incoming vector and each of the stored support vectors. The three-transistor unit cell in the array combines single-bit dynamic storage, binary multiplication, and zero-latency analog accumulation. Precise digital outputs are obtained through oversampled quantization of the analog array outputs combined with bit-serial unary encoding of the digital inputs. The 256 input, 128 vector Kerneltron measures 3 mm/spl times/3mm in 0.5 /spl mu/m CMOS, delivers 6.5 GMACS throughput at 5.9 mW power, and attains 8-bit output resolution.
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