Project description
Pioneering solutions for tomorrow’s IoT
In the ever-evolving landscape of neuromorphic engineering, it’s important to bridge the gap between biological brain complexities and custom silicon. While memristors showed potential to tackle synapse density issues, their scalability was constrained by power dissipation and chip size. In this context, the MSCA-funded EEHIMIC project aims to revolutionise energy efficiency within neural networks, setting the stage for a new era of intelligent Internet of Things (IoT) applications. By leveraging cutting-edge FDSOI28nm technology, neurons are designed for optimised energy consumption. Further innovations include rapid switching pulses for low-current synapse activation. With dedicated PCB integration, EEHIMIC aspires to empower unsupervised learning, enabling energy-frugal recognition of letters and digits.
Objective
Neuromorphic engineering is an emerging bio-inspired discipline that morphs the biological brain on custom silicon. Although memristors rose as a potential synapse to solve the density challenge in a memristive crossbar, the scalability of the crossbar is limited by its power dissipation and chip area. To contribute to low power dissipation, I focus on improving the energy efficiency of the synapses (non-filamentary category) and neurons, which are the fundamental constituents of the neural network-enabled IOTs. The energy efficiency of the synapses will be improved by applying fast switching pulses on the bulk-based synapses that result in low switching currents. The energy efficiency of the neurons will be improved by taking advantage of the FDSOI28nm technology node by which the neurons are designed. A dedicated PCB will be designed, assembled and mounted that house both the synapses, and the neurons, which will be energy-efficiently used to recognize digits or letters by unsupervised learning rule.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
- natural sciencescomputer and information sciencesinternetinternet of things
- natural scienceschemical sciencesinorganic chemistrymetalloids
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Programme(s)
- HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA) Main Programme
Funding Scheme
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European FellowshipsCoordinator
20133 Milano
Italy