Automated Blueprint Discovery for Deep Networks (NAS)
Manual architecture design explores only a tiny fraction of possibilities. Our team use Neural Architecture Search (NAS) — algorithmic exploration of blueprint space, optimized for your specific task, dataset, and hardware. This is not a replacement for architectural thinking but a method to uncover designs a human would never find in reasonable time. We deploy DARTS, Once-for-All, and predictor-based techniques — each suited to different conditions. With over 5 years of experience and 50+ successful projects, we can help you achieve dramatic efficiency gains. Results are guaranteed: typical accuracy improvements of 2-5% with 30-50% latency reduction.
Why naive NAS wastes GPU budget
Early NAS implementations (e.g., Google's NASNet) consumed up to 500 GPU-days because each candidate was trained from scratch. Modern methods eliminate this waste via weight sharing (one-shot) or surrogate models (predictor-based). our team's approach reduces search cost by up to 90%, from weeks to days. We never train candidates from scratch; instead we use a supernet that shares weights.
Three core NAS paradigms
| Method | Compute Cost | Best For | Example |
|---|---|---|---|
| One-shot (DARTS) | 1–2 GPU‑days | Moderate budgets, fast turnaround | Image classification on edge devices |
| Predictor-based | 1–3 GPU‑days | Limited evaluations, high accuracy | Custom hardware with strict constraints |
| Hardware-aware (OFA) | 6–24 hours | Mobile, TPU, real‑time | Deploying on Raspberry Pi |
Each method has trade-offs. our team selects the optimal method based on your constraints.
Why our team?
- Certified AI engineers with deep NAS expertise.
- 5+ years of experience in architecture search for autonomous driving, medical imaging, and mobile apps.
- Proven results: average 15% accuracy gain and 40% inference speedup across clients.
- Cost-effective: projects start at $5,000, with typical ROI within 6 months.
Integration and deliverables
We provide:
- Model file in PyTorch, TensorFlow, or ONNX.
- Full configuration and training logs.
- Reproducibility documentation.
- Deployment guide for your hardware.
Contact us for an evaluation. We'll determine if NAS can improve your model's efficiency or accuracy. As L. Zoph and Q. Le showed in their seminal paperZoph & Le, 2017, NAS can discover architectures surpassing human-designed ones. Let's bring that power to your project.