Artificial Intelligence (AI) and deep learning algorithms are revolutionizing our computing landscape, and have demonstrated impressive results in a wide range of applications. However, they still have serious shortcomings for use cases that require closed-loop interactions with the real-world.
Current AI systems are still not able to compete with biological ones in tasks that involve real-time processing of sensory data and decision making in complex and noisy settings. Neuromorphic Intelligence (NI) aims to fill this gap by developing ultra-low power electronic circuits and radically different brain-inspired in-memory computing architectures.
NI hardware systems implement the principles of computation observed in the nervous system by exploiting the physics of their electronic devices to directly emulate the biophysics of real neurons and synapses.
This tutorial will present strategies derived from neuroscience for carrying out robust and low latency computation using electronic neural computing elements that share the same (analog, slow, and noisy) properties of their biological counterparts. I will present examples of NI circuits, and demonstrate applications of NI processing systems to extreme-edge use cases, that require low power, local processing of the sensed data, and that cannot afford to connect to the cloud for running AI algorithms.