Advancing Image Classification with Phase-coded Ultra-Efficient Spiking Neural Networks
Bibliographic record
Abstract
Conventional surrogate Back-Propagation-Through-Time learning in Spiking Neural Networks (SNN) demands excessive energy consumption when simulating over extended time intervals. Moreover, the spike encoding process necessitates intricate hardware support, thus undermining overall efficiency. Additionally, their classification accuracies fall short in comparison to artificial neural networks due to the inherent information loss in spike translation. Therefore, there is a critical need for efficient techniques that can enhance performance without compromising accuracy. In this study, we introduce a novel learning scheme that harnesses lossless phase coding. This approach allows us to achieve minimal inference latency, requiring a maximum of at most 8 simulation steps. Furthermore, our training times exhibit significant reductions when compared to previous single-spike networks. Our experimental results demonstrate that Phase-SNN attains state-of-the-art accuracy levels, achieving 98.6% and 89.6% accuracies on the MNIST and Fashion-MNIST datasets, respectively.
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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 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".