EEHIMIC
Financiado
Energy-efficient hardware implementation of memristor-based in-memory computing
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 scala...
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.
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Duración del proyecto: 37 meses
Fecha Inicio: 2023-07-03
Fecha Fin: 2026-08-31
Fecha Fin: 2026-08-31
Línea de financiación: concedida
El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2023-07-03
Línea de financiación objetivo
El proyecto se financió a través de la siguiente ayuda:
HORIZON-MSCA-2022-PF-01-01:
MSCA Postdoctoral Fellowships 2022
Cerrada
hace 2 años
Presupuesto
El presupuesto total del proyecto asciende a
189K€
Líder del proyecto
POLITECNICO DI MILANO
No se ha especificado una descripción o un objeto social para esta compañía.
Perfil tecnológico
TRL
4-5
Perfil tecnológico
TRL
4-5
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Participante
N.D. |
Participante