DOIONLINE 
DOIONLINE Publish In |
International Journal of Advance Computational Engineering and Networking (IJACEN)-IJACEN |
 Journal Home Volume Issue |
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Issue |
Volume-12,Issue-9 ( Sep, 2024 ) | |||||||||
Paper Title |
Enabling On-Line Precision Scaling for Energy-Driven Adaptive ConvNets | |||||||||
Author Name |
Valentino Peluso, Andrea Calimera, Enrico Macii | |||||||||
Affilition |
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Pages |
45-51 | |||||||||
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 | |||||||||
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