Analog Programmable Distance Calculation Circuit for Winner Takes All Neural Network Realized in the CMOS Technology
Bibliographic record
Abstract
This paper presents a programmable analog current-mode circuit used to calculate the distance between two vectors of currents, following two distance measures. The Euclidean (L2) distance is commonly used. However, in many situations, it can be replaced with the Manhattan (L1) one, which is computationally less intensive, whose realization comes with less power dissipation and lower hardware complexity. The presented circuit can be easily reprogrammed to operate with one of these distances. The circuit is one of the components of an analog winner takes all neural network (NN) implemented in the complementary metal-oxide-semiconductor 0.18- [Formula: see text] technology. The learning process of the realized NN has been successfully verified by the laboratory tests of the fabricated chip. The proposed distance calculation circuit (DCC) features a simple structure, which makes it suitable for networks with a relatively large number of neurons realized in hardware and operating in parallel. For example, the network with three inputs occupies a relatively small area of 3900 μm(2). When operating in the L2 mode, the circuit dissipates 85 [Formula: see text] of power from the 1.5 V voltage supply, at maximum data rate of 10 MHz. In the L1 mode, an average dissipated power is reduced to 55 [Formula: see text] from 1.2 V voltage supply, while data rate is 12 MHz in this case. The given data rates are provided for the worst case scenario, where input currents differ by 1%-2% only. In this case, the settling time of the comparators used in the DCC is quite long. However, that kind of situation is very rare in the overall learning process.
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 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".