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About this paper
Wang Qinsi, Ke Jinghan, Liang Zhi, Zhang Sihai
MathNAS introduces a general and efficient framework for Neural Architecture Search that utilizes mathematical programming to design high-performing neural networks. The approach accurately predicts network performance across diverse architectures while significantly reducing search complexity and enabling real-time adaptation for on-device deployment.
Key insights
- Network performance (accuracy, latency, energy) can be accurately predicted from the aggregated and empirically mapped performances of its constituent blocks.
- The change in a network's accuracy due to a block swap is approximately inversely proportional to the network's FLOPs, indicating that smaller networks are more sensitive to architectural modifications.
- Reliable block performance estimation for the prediction model can be achieved by evaluating the block's impact within a single, representative 'average-FLOPs network' rather than averaging over numerous network configurations.
Summary by alphaXiv.
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