# Energy First Arch Eval

> Evaluates the classification accuracy and training energy efficiency of biologically-inspired and physics-guided neural architectures against conventional baselines across diverse data modalities. It probes whether action-principle regularization yields modality-specific performance gains and reduced internal activation energy without accuracy loss. Use when the user wants to benchmark on Fashion-MNIST, CIFAR-10, DVS Gesture, SHD, SSC, WESAD, DREAMER, SEED-IV, 20newsgroups, or asks about evaluating this task. Reports Accuracy.

- Skill: `qhjqhj00/energy-first-arch-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/energy-first-arch-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/energy-first-arch-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/energy-first-arch-eval

---


# energy-first-arch-eval

> minAction.net: Energy-First Neural Architecture Design -- From Biological Principles to Systematic Validation — Frasch (2026) (arXiv:2604.24805, 2026)

## What this evaluates

Evaluates the classification accuracy and training energy efficiency of biologically-inspired and physics-guided neural architectures against conventional baselines across diverse data modalities. It probes whether action-principle regularization yields modality-specific performance gains and reduced internal activation energy without accuracy loss.

## Datasets

- **Fashion-MNIST** — total ?; splits: test (-1)
- **CIFAR-10** — total ?; splits: test (-1)
- **DVS Gesture** — total ?; splits: test (-1)
- **SHD** — total ?; splits: test (-1)
- **SSC** — total ?; splits: test (-1)
- **WESAD** — total ?; splits: test (-1)
- **DREAMER** — total ?; splits: test (-1)
- **SEED-IV** — total ?; splits: test (-1)
- **20newsgroups** — total ?; splits: test (-1)

## Metrics

- `Accuracy` **(primary)** — range: percent
  - Proportion of correctly classified instances out of the total number of instances.
- `mJ/correct` — range: other
  - Total training energy in millijoules divided by the number of correct predictions.
- `Activation Energy (relative)` — range: other
  - Hardware-independent proxy computed as the expected L2 norm of layer activations, normalized to baseline.

## Input / output format

**Input**: Standard dataset inputs (images, text, or neuromorphic spike trains) depending on the modality.

**Output**: Class predictions for each instance.

## Scoring recipe

```python
def compute_metrics(predictions, gold, energy_joules):
    correct = sum(1 for p, g in zip(predictions, gold) if p == g)
    accuracy = correct / len(gold)
    mJ_per_correct = (energy_joules * 1000) / correct if correct > 0 else float('inf')
    return {'accuracy': accuracy, 'mJ_per_correct': mJ_per_correct}
```

## Common pitfalls

- Confusing task difficulty and convergence speed with intrinsic architectural energy efficiency.
- Averaging metrics across modalities, which obscures strong architecture-dataset interactions (partial η² = 0.439).
- Measuring only inference energy, whereas reported gains stem from training dynamics (fewer epochs).

## Evidence (verbatim from paper)

> Architecture significantly impacted energy efficiency (F(3,1381)=19.11, p<0.001). CNN showed the lowest energy-per-correct (7,059 mJ/correct ± 56 SE), compared to BimodalTrue (103,461 mJ/correct ± 6,465 SE), MLP (137,303 mJ/correct ± 10,303 SE), and Physics-Lagrangian (102,966 mJ/correct ± 6,087 SE). However, this apparent advantage is primarily attributable to task difficulty and faster convergence on easier vision benchmarks rather than intrinsic architectural efficiency, as detailed in the next paragraph.

## Citation

```bibtex
@misc{frasch2026minactionnet,
  title={minAction.net: Energy-First Neural Architecture Design -- From Biological Principles to Systematic Validation},
  author={Frasch (2026)},
  year={2026},
  note={arXiv:2604.24805}
}
```

- arXiv: 2604.24805