Design and Analysis of a Frequency-Driven LIF Model Neuron
Bibliographic record
Abstract
Spiking Neural Networks (SNNs) hold significant potential for achieving low-power computational capabilities in artificial intelligence (AI) applications. In this brief a low-power frequency-driven mixed-mode complementary metal-oxide-semiconductor (CMOS) circuit based on the leaky integrate-and-fire (LIF) neuron model is proposed. The dynamic behavior of the proposed structure is modeled by the frequency adjustment and the proposed circuit can model dynamic behavior without the need for an external voltage source. Furthermore, the mixed-mode circuit is not sensitive to process, voltage, and temperature (PVT) variation effects. Tailored for large-scale SNN deployment and neuromorphic algorithm realization, the design draws inspiration from a monostable block to generate spikes in the neural model’s output. The proposed circuit consists of two main parts: the neuron circuit generating output spikes and a transmission circuit connecting neurons. Simulation results vividly showcase various spiking behaviors akin to those observed in biological neurons. Noteworthy is the careful crafting of the neuron model using a 45 nm CMOS process, resulting in a mere 2.4 nW power consumption with a 1.4 V headroom voltage for multi-spiking events.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".