Optimization techniques for Artificial Neural Networks in microcontroller Edge- Computing systems

Key Points

• Improved performance in artificial neural networks leads to better efficiency in microcontroller applications.
• Optimization techniques enhance processing capabilities and energy efficiency in edge-computing systems.
• Assessment of various optimization methods applied in edge-computing environments for neural networks demonstrates their effectiveness.
• The findings highlight potential advancements in practical applications of artificial neural networks at the edge.

Abstract