# Split Computing Eval

> Evaluates a multi-task supervised compression model for split computing across image classification, object detection, and semantic segmentation. It measures predictive accuracy alongside system-level metrics like end-to-end latency and energy consumption on resource-constrained edge devices with simulated wireless links. Use when the user wants to benchmark on ILSVRC 2012, COCO 2017, PASCAL VOC 2012, or asks about evaluating this task. Reports model accuracy.

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

---


# split-computing-eval

> A Multi-task Supervised Compression Model for Split Computing — Matsubara et al. (2025) (arXiv:2501.01420, 2025)

## What this evaluates

Evaluates a multi-task supervised compression model for split computing across image classification, object detection, and semantic segmentation. It measures predictive accuracy alongside system-level metrics like end-to-end latency and energy consumption on resource-constrained edge devices with simulated wireless links.

## Datasets

- **ILSVRC 2012** — total ?; splits: test (-1)
- **COCO 2017** — total ?; splits: test (-1)
- **PASCAL VOC 2012** — total ?; splits: test (-1)

## Metrics

- `encoder size` — range: other
  - Total size of the shared lightweight encoder parameters, typically calculated as the number of parameters multiplied by 4 bytes (assuming FP32 precision).
- `compressed data size` — range: other
  - Total number of bytes transmitted over the wireless link for the encoded representation, measured in megabytes (MB).
- `model accuracy` **(primary)** — range: [0, 1]
  - Standard predictive accuracy for each task: Top-1 accuracy for image classification, mean Average Precision (mAP) for object detection, and mean Intersection over Union (mIoU) for semantic segmentation.
- `end-to-end latency` — range: other
  - Total time from input image ingestion to final task prediction output, including encoding, transmission over the simulated wireless link, and server-side decoding/inference.
- `energy consumption` — range: other
  - Total energy in Joules consumed by the mobile device and edge server during inference, calculated by integrating instantaneous power readings using Simpson’s rule.

## Input / output format

**Input**: Raw images from ILSVRC 2012, COCO 2017, or PASCAL VOC 2012, passed through a shared lightweight encoder for split computing and transmitted over a simulated wireless link (100 Kbps or LoRa at 37.5 Kbps) to an edge server.

**Output**: Task-specific predictions (class labels, bounding boxes, or segmentation masks) and system-level measurements (end-to-end latency in seconds, energy consumption in Joules).

## Scoring recipe

```python
def compute_metrics(predictions, gold, power_trace, start_time, end_time, encoder, transmissions):
    acc = compute_task_accuracy(predictions, gold)  # Top-1/mAP/mIoU
    latency = end_time - start_time
    energy = simpson_integrate(power_trace)
    encoder_size = encoder.num_parameters() * 4
    compressed_size = sum(len(p) for p in transmissions)
    return {'accuracy': acc, 'latency': latency, 'energy': energy, 'encoder_size': encoder_size, 'compressed_size': compressed_size}
```

## Common pitfalls

- Failing to warm up devices before measuring latency and power consumption, leading to unstable GPU behavior and inaccurate readings.
- Confusing encoder size (model parameters) with compressed data size (transmitted payload), which are distinct metrics in split computing.
- Ignoring the specific wireless data rate (100 Kbps vs 37.5 Kbps) when calculating transmission latency, which significantly impacts end-to-end timing.

## Evidence (verbatim from paper)

> Our evaluations are based on the benchmark framework for split computing in[[24]], which adopts three key metrics: 1) encoder size, 2) compressed data size, and 3) model accuracy. Additionally, we consider end-to-end latency and energy consumption of mobile devices in multi-task scenarios.

## Citation

```bibtex
@misc{matsubara2025multitask,
  title={A Multi-task Supervised Compression Model for Split Computing},
  author={Matsubara et al. (2025)},
  year={2025},
  note={arXiv:2501.01420}
}
```

- arXiv: 2501.01420