Enhanced regularization for on-chip training using analog and temporary memory weights.

In-memory computing techniques are used to accelerate artificial neural network (ANN) training and inference tasks. Memory technology and architectural innovations allow efficient matrix-vector multiplications, gradient calculations, and updates to network weights. However, on-chip learning for edge devices is quite challenging due to the frequent updates.

Gecko adhesion based sea star crawler robot.

Over the years, efforts in bioinspired soft robotics have led to mobile systems that emulate features of natural animal locomotion. This includes combining mechanisms from multiple organisms to further improve movement. In this work, we seek to improve locomotion in soft, amphibious robots by combining two independent mechanisms: sea star locomotion gait and gecko adhesion.