Soufiane Mourrane
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Soufiane Mourrane has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Andreia Cathelin
ST Microelectronics, Crolles, France
1 shared publication - Antoine Frappe
CNRS, Centrale Lille, Junia, Universit de Lille, Lille, France
1 shared publication - Benoit Larras
CNRS, Centrale Lille, Junia, Universit de Lille, Lille, France
1 shared publication - Sylvain Clerc
STMicroelectronics, Crolles, France
1 shared publication
Latest Publications
- 8 November 2024
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Journal of Solid-State Circuits
- Vol. 60 (6) , 2060-2071
A Sub-400-nW Real-Time Event-Driven Spectrogram Extraction Unit in 28-nm FD-SOI CMOS for Keyword Spotting Application
Considering the power-hungry nature of speech processing, a keyword spotting (KWS) unit, used to detect multiple spoken words, is often integrated as a front-end layer. KWS systems are always active, and thus, it is extremely important to optimize the devoted power budget. In this context, this article presents a programmable low-power event-driven real-time spectrogram extraction unit tested for the KWS application. This chip, fabricated in 28-nm FD-SOI CMOS technology, has been combined with a software-defined convolutional neural network to demonstrate the recognition of 11 audio classes (ten keywords $+$ background $+$ unknown) with an accuracy equal to 87.9% and an activity-dependent power consumption measured at 391.6 nW, for a 12-keyword/min average speech rate.
- 8 November 2024
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Journal of Solid-State Circuits
- Vol. 60 (6) , 2060-2071
A Sub-400-nW Real-Time Event-Driven Spectrogram Extraction Unit in 28-nm FD-SOI CMOS for Keyword Spotting Application
Considering the power-hungry nature of speech processing, a keyword spotting (KWS) unit, used to detect multiple spoken words, is often integrated as a front-end layer. KWS systems are always active, and thus, it is extremely important to optimize the devoted power budget. In this context, this article presents a programmable low-power event-driven real-time spectrogram extraction unit tested for the KWS application. This chip, fabricated in 28-nm FD-SOI CMOS technology, has been combined with a software-defined convolutional neural network to demonstrate the recognition of 11 audio classes (ten keywords $+$ background $+$ unknown) with an accuracy equal to 87.9% and an activity-dependent power consumption measured at 391.6 nW, for a 12-keyword/min average speech rate.