Architecture Search Inputs
Enter workload, search, model, and deployment assumptions. After submission, the calculated result appears above this form.
Example Data Table
Use this sample to understand how architecture family, strategy, and deployment targets shift the final ranking.
| Scenario | Family | Strategy | Params (M) | Latency (ms) | Memory (MB) | Accuracy Proxy (%) | Score |
|---|---|---|---|---|---|---|---|
| Image classification baseline | Hybrid | Bayesian | 28.40 | 12.80 | 986.00 | 87.60 | 84.30 |
| Fast edge deployment | CNN | Random | 11.20 | 5.10 | 412.00 | 79.90 | 81.70 |
| Large search for research | Transformer | Differentiable | 46.80 | 21.60 | 1680.00 | 91.20 | 78.40 |
| Balanced production candidate | EfficientNet-like | Evolutionary | 19.60 | 8.90 | 702.00 | 85.10 | 86.00 |
Formula Used
This tool is an estimation framework for comparing search directions before expensive training. It does not replace a true benchmark run.
1) Estimated Parameters
Parameters ≈ stem cost + Σ(stage blocks × stage width² × expansion ratio × kernel factor × family multiplier) + classification head.
2) Estimated FLOPs
FLOPs ≈ parameters × resolution scale × task complexity × stage multiplier × family complexity.
3) Estimated Latency
Latency ≈ FLOPs ÷ effective hardware throughput. Effective throughput changes with TOPS, precision mode, and batch influence.
4) Estimated Memory
Memory ≈ parameter memory + activation memory + training overhead. Lower precision reduces memory pressure and often improves speed.
5) Search Time
Search Time ≈ trials × epochs per trial × dataset size ÷ estimated throughput × strategy overhead.
6) Composite Score
Score = weighted average of accuracy fit, latency fit, memory fit, and search efficiency fit. Your input weights decide the ranking.
How to Use This Calculator
- Choose the task type and search strategy that match your project.
- Select the model family you want to explore.
- Enter input dimensions, dataset size, classes, and architecture depth settings.
- Define deployment constraints such as target latency, memory limit, precision, and hardware TOPS.
- Adjust the objective weights to reflect what matters most in your workflow.
- Click the submit button to generate the result summary above the form.
- Review the graph and internal comparison table to see tradeoffs across candidate profiles.
- Export the comparison with CSV or PDF when you need a shareable report.
FAQs
1) What does this tool actually optimize?
It balances four goals: predicted accuracy, inference latency, memory budget, and search cost. Your chosen weights determine which candidate profile ranks highest.
2) Is the accuracy value a real benchmark result?
No. It is a comparative proxy based on model capacity, data scale, task difficulty, and efficiency penalties. Use it to rank options before real experiments.
3) Why does latency change when precision changes?
Lower precision usually reduces memory traffic and improves arithmetic throughput. That often lowers latency, especially on accelerators optimized for fp16 or int8 inference.
4) Which search strategy is usually fastest?
Differentiable search often has the lowest total exploration cost in this estimator. Random search is simple, while evolutionary and reinforcement methods usually need more trials.
5) When should I choose a hybrid family?
Hybrid families work well when you want convolutional locality plus attention-based global context. They are often a balanced choice for vision tasks with moderate deployment budgets.
6) What if my memory budget is very small?
Reduce base width, use fewer blocks, lower the batch size, and switch to fp16 or int8. These changes usually produce the biggest memory savings first.
7) Can I use this for NLP or time series models?
Yes. The task selector adjusts complexity assumptions so the comparison remains useful, though final production benchmarking should still be done on your actual workload.
8) What should I export after analysis?
Export the comparison table. It gives a compact view of candidate profiles, estimated compute cost, search time, deployment fit, and total weighted score.