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TEVI

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Time Encoded Voice Interfaces

The research of this EID focuses on ultra-low power sensors incorporating artificial intelligence. The current solution for such systems requires an analog-to-digital converter (ADC) prior to the signal processing block, usually i... ver más

30/04/2025

UC3M

753K€

Presupuesto del proyecto: 753K€

Líder del proyecto

UNIVERSIDAD CARLOS III DE MADRID No se ha especificado una descripción o un objeto social para esta compañía.

Total investigadores 1335

Fecha límite participación Sin fecha límite de participación.

Ver 2 Participantes

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2020-07-08

MSCA-ITN-2020: Innovative Training Networks

I+D

Cerrada hace 5 años

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Información adicional privada

No hay información privada compartida para este proyecto. Habla con el coordinador.

2 Participantes

UC3M

752.71K€ | Lider

IFAT

N.D. | Participante

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Duración del proyecto: 57 meses Fecha Inicio: 2020-07-08
Fecha Fin: 2025-04-30

Líder del proyecto

UNIVERSIDAD CARLOS III DE MADRID No se ha especificado una descripción o un objeto social para esta compañía.

Total investigadores 1335

Presupuesto del proyecto 753K€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto The research of this EID focuses on ultra-low power sensors incorporating artificial intelligence. The current solution for such systems requires an analog-to-digital converter (ADC) prior to the signal processing block, usually implemented with a neural network (NN). The innovation consists of removing the ADC prior to the NN by directly coupling the sensor to it and encoding the sensor signals with a voltage-controlled-oscillator (VCO). VCO-based ADCs have been used to implement integrated sensors. Achieving this goal requires to develop a new multiply-accumulate cell (MAC) for the first layer of the NN that operates with signals from the VCO, and a suitable VCO interfaces with existing sensors. In most applications, the raw data form the sensor is required as well. Here, signals coming from the VCO can also be converted to a sampled sequence by enabling a digital decoder, which is not needed when detecting a pattern in the NN. As a benefit, power consumption can meet the requirements of battery-operated products. Power improvement comes from both the ADC removal and the power efficiency of the NN implementation. Approaches to implement a sensor interface using a VCO and to implement a phase/frequency-encoded MAC unit (P-MAC) for a NN have been attempted separately but, there is no combination of both ideas. The research in this EID tries to bridge this gap. This architecture can be useful for both research and industrial applications, such as neural probe chips, wearable electronics or battery powered IoT devices. This EID proposal requires intersectoral involvement of both academia and the industry, to develop a doctoral program and train researchers that will be in high demand by having the specific skills developed in this research. We have selected waterproof smart microphones as an application to benefit from this research, which may directly lead to a product development of interest to microelectronic industries in the EU producing MEMS microphones.

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