Show HN: Energy-Efficient NAS via RBF Kernel Scoring (No GPU Training Needed)

We present RBFleX-NAS, a training-free neural architecture search (NAS) framework that leverages an RBF kernel-based scoring mechanism to rank neural networks without training. Unlike traditional NAS methods that consume significant GPU resources, RBFleX-NAS can find high-performing architectures in seconds.

Key Applications:
• Edge AI Deployment: Efficient model search for Raspberry Pi, Jetson, and mobile platforms
• AutoML Integration: Lightweight NAS module suitable for AutoML systems
• Cross-domain Transfer: Supports both vision and NLP tasks (e.g., TransNAS-Bench)

How it works:
Instead of training each candidate network, we score architectures using a Radial Basis Function (RBF) kernel that captures both activation patterns and structural features.

Demo Video:
https://youtu.be/QZz8s95x9xw?si=fqVs7T6no66zz_d5

Code on GitHub:
https://github.com/tomomasayamasaki/RBFleX-NAS

Paper
https://ieeexplore.ieee.org/document/10959729/metrics

Comments URL: https://news.ycombinator.com/item?id=44546386

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