Supernetwork-based efficient mapping of deep learning applications to mixed-precision hardware using model adaptation.

Abstract

The rapid proliferation of Artificial Intelligence applications necessitates scalable solutions that perform efficiently under real-world constraints. Heterogeneous accelerators combining specialized analog and digital units offer localized, energy-efficient neural network computations. However, achieving optimal performance on these platforms requires balancing energy efficiency and model accuracy through optimized neural network layer mapping. To this end, we introduce Mixed-Precision Supernetwork, a unified framework for training mixed-precision supernetworks that seamlessly integrate quantized layers with analog noise-sensitive layers. Mixed-Precision Supernetwork incorporates a mapping-aware adaptation strategy, dynamically optimizing layer assignments while refining the neural network via hardware-aware architecture search. This dual innovation establishes Mixed-Precision Supernetwork as a groundbreaking approach for deploying deep learning models efficiently on heterogeneous accelerators. On average, Mixed-Precision Supernetwork produces mappings  ~ 2.2 × faster and achieves a  ~ 3.4% increase in model accuracy over a fully analog approach, while improving energy-efficiency by mapping up to 80% of the model's weights to analog hardware while maintaining full-precision accuracy.