Paper Title
Enabling On-Line Precision Scaling for Energy-Driven Adaptive ConvNets

Abstract
Adaptive Convolutional Deep Neural Networks (Adaptive ConvNets) can reshape their behavior to reach a better trade-off between computational effort and prediction accuracy, which is a key feature for energy-efficient edge applications. As a subclass, energy-driven adaptive ConvNets can self-tune their energy footprint upon request based on an external trigger produced at the application level. This work introduces a design and optimization strategy based on the concept of online precision scaling. The optimization was built and formulated as a multi-objective problem solved via a modified version of the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) that guarantees a fast design space exploration. The simulation results we collected using a software-programmable neural accelerator architecture with mixed-precision arithmetic demonstrate our approach enables ConvNets to shift over more Pareto optimal operating points, with energy savings up to 35% for less than 3% accuracy loss. Keywords - Deep Learning, Optimization, Energy Efficiency.