Session

DescriptionThis session covers advancements in optimizing deep learning training and inference, with a focus on edge-device performance, low-precision training, neuromorphic computing, and hardware-software co-design. The papers presented here explore efficient techniques that reduce model complexity, energy consumption, and improve robustness, enabling better performance for edge and specialized systems.