Energy-efficient AI

We aim at bringing AI from data centers to the edge. Lower energy consumption will improve battery-powered devices autonomy. Moreover, no need for data transfer over the network will ultimately enhance privacy and provide better data security.

To help you getting familiar with the subject, here is an introductory post on binarized neural networks.


. Reservoir computing with biocompatible organic electrochemical networks for brain-inspired biosignal classification. Science Advances, 2021.

PDF Project Project Publication IEEE Spectrum

. In-Memory Resistive RAM Implementation of Binarized Neural Networks for Medical Applications. At DATE, 2020.

Preprint Project Publication

. Digital Biologically Plausible Implementation of Binarized Neural Networks with Differential Hafnium Oxide Resistive Memory Arrays. In Front. Neurosci., 2020.

Preprint PDF Project Project Publication

. Stochastic Computing for Hardware Implementation of Binarized Neural Networks. IEEE Access, 2019.

Preprint PDF Project


Medical Applications of Low Precision Neuromorphic Systems
Sep 26, 2019