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A Versatile Time-Domain Winner-Take-All Spike Decoder for Neuromorphic Systems

2025· article· W4417169843 on OpenAlexafffund
Pedro Sartori Locatelli, Mahsa Zareie, Dalton Martini Colombo, Michael S. Freund, Kamal El‐Sankary

Bibliographic record

Venuenot available
Typearticle
Language
FieldEngineering
TopicAdvanced Memory and Neural Computing
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNeuromorphic engineeringSpike (software development)CMOSBlock (permutation group theory)ConvertersComputationPower (physics)Decoding methodsSpiking neural network

Abstract

fetched live from OpenAlex

This article presents a time-domain spike decoder for neuromorphic hardware that combines spike processing with winner-take-all (WTA) operation, facilitating decision-making in spiking neural networks (SNNs). By integrating both functions into a single block and exploiting time-mode signal properties, the circuit supports fully parallel computation regardless of input size and eliminates the need for counters or converters used in conventional approaches. The decoder is versatile, as it can select the winning neuron based on either the highest spike count or the largest pulse width. Implemented and simulated in commercial 180-nm CMOS technology, it achieves efficient silicon area utilization, competitive power consumption, and operating speed on par with standalone state-of-the-art WTA circuits.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.755
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.026
GPT teacher head0.252
Teacher spread0.226 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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