Automated Blueprint Discovery for Deep Networks (NAS)
Manual architecture design explores only a tiny fraction of possibilities. At TrueTech, we 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). TrueTech'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. TrueTech selects the optimal method based on your constraints.
Why TrueTech?
- 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 a free 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.