# Ma Snn Eval

> Evaluates the effectiveness and energy efficiency of Multi-dimensional Attention (MA) modules integrated into Spiking Neural Networks (SNNs) for event-based action recognition and static image classification. Use when the user wants to benchmark on DVS128 Gesture, DVS128 Gait, ImageNet-1K, or asks about evaluating this task. Reports Top-1 Accuracy (%).

- Skill: `qhjqhj00/ma-snn-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/ma-snn-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/ma-snn-eval/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Productivity
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/ma-snn-eval

---


# ma-snn-eval

> Attention Spiking Neural Networks — Man Yao et al. (2022) (arXiv:2209.13929, 2022)

## What this evaluates

Evaluates the effectiveness and energy efficiency of Multi-dimensional Attention (MA) modules integrated into Spiking Neural Networks (SNNs) for event-based action recognition and static image classification.

## Datasets

- **DVS128 Gesture** — total 1342; splits: test (-1)
- **DVS128 Gait** — total 4200; splits: test (-1)
- **ImageNet-1K** — total 1330000; splits: train (1280000), test (50000)

## Metrics

- `Top-1 Accuracy (%)` **(primary)** — range: percent
  - Percentage of correctly classified samples out of the total test set.
- `Energy Efficiency ($r_{EE}$)` — range: ratio
  - Ratio of baseline or ANN energy consumption to the SNN's energy consumption.
- `NASAR` — range: [0, 1]
  - Normalized Average Spiking Activity Rate, representing the average fraction of neurons firing per time step.

## Input / output format

**Input**: Event streams (DVS datasets) or static images (ImageNet-1K) processed by SNN backbones.

**Output**: Class predictions (for classification tasks).

## Scoring recipe

```python
def compute_metrics(predictions, gold_labels, spike_counts, baseline_energy, num_neurons, time_steps):
    accuracy = (predictions == gold_labels).sum() / len(gold_labels) * 100
    energy_efficiency = baseline_energy / spike_counts
    nasar = spike_counts / (num_neurons * time_steps)
    return {'accuracy': accuracy, 'energy_efficiency': energy_efficiency, 'nasar': nasar}
```

## Common pitfalls

- Energy efficiency is reported as a relative ratio ($r_{EE}$) against a baseline or ANN, not absolute hardware energy consumption.
- NASAR (Normalized Average Spiking Activity Rate) measures average spiking activity per neuron per time step, which differs from total spike counts.
- Latency ($T$) is not fixed across experiments; varying $T$ drastically changes both accuracy and energy metrics, so comparisons must control for time steps.

## Evidence (verbatim from paper)

> In Table[I](#S5.T1 "TABLE I ‣ 5 Experiments ‣ Attention Spiking Neural Networks"), we report the accuracy of each vanilla model and its attention counterpart, and compare TCSA-SNN with previous works. We observe that in every comparison, TCSA-SNN outperforms the vanilla architectures, suggesting that the benefits of attention modules are not confined to a single event-based dataset, limited base architecture, or fixed output latency.

## Citation

```bibtex
@misc{yao2022attention,
  title={Attention Spiking Neural Networks},
  author={Man Yao et al. (2022)},
  year={2022},
  note={arXiv:2209.13929}
}
```

- arXiv: 2209.13929