DOI: https://doi.org/10.29363/nanoge.matnec.2022.002
Publication date: 23rd February 2022
Artificial Intelligence (AI) and deep learning algorithms have demonstrated impressive results in a wide range of applications. However, they still have serious shortcomings for use cases that require real-time processing of sensory data and closed-loop interactions with the real-world, in uncontrolled environments.
Neuromorphic Intelligence (NI) aims to mitigate this shortcoming by developing ultra-low power electronic circuits and radically different brain-inspired in-memory computing architectures.
In this presentation I will present examples of NI circuits that exploit the physics of their devices to directly emulate the biophysics of real neurons, and I will demonstrate applications of NI processing systems to 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.
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