Ultra low noise signed digit arithmetic using cellular neural networks
Bibliographic record
Abstract
This paper addresses mixed-signal applications where the presence of digital switching noise is a major problem; for example, digital circuitry adjacent to sensitive bio-sensors in an SoC device. This paper describes a method for building ultra low-noise signed-digit arithmetic circuits using analog cellular neural networks, essentially implementing asynchronous digital logic with analog circuits. Each node in our asynchronous architectures uses controlled current sources driving into capacitors; providing both low current and voltage time derivatives (di/dt and dv/dt) and, as a result, reducing both instantaneous and average system and cross-talk noise. In this paper, we present the architecture of a signed-digit radix-2 adder with symmetrical digit set {-1,0,1}. The adder uses a new class of CNNs that has three stable states to match the three values of the digit set. The adder not only has all the known advantages of SD addition, but also greatly reduces switching noise. We also describe a 32x32-digit multiplier based on this technique. In a simulated comparison with CMOS digital counterparts in a 0.35/spl mu/m CMOS technology, the peak system noise is 60-70dB lower for the CNN circuits.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".