7 citations · 14 across the 7 of their papers we have counts for
7 papers
Bringing Chip Tapeout Into University Education
Luca Pezzarossa, Martin Schoeberl, Matti Käyrä +5
Providing students with experience from chip specification to fabricated silicon can strengthen chip-design education, but integrating tapeout into regular teaching is difficult to…
What changes after deployment? A survey on On-device Learning in TinyML
Massimo Pavan, Luca Pezzarossa, Fabrizio Pittorino +2
Machine learning models on microcontroller-class devices (TinyML) face a fundamental challenge: post-deployment distribution change undermines static models. On-device learning (OD…
From Diet to Free Lunch: Estimating Auxiliary Signal Properties using Dynamic Pruning Masks in Speech Enhancement Networks
Riccardo Miccini, Clément Laroche, Tobias Piechowiak +2
Speech Enhancement (SE) in audio devices is often supported by auxiliary modules for Voice Activity Detection (VAD), SNR estimation, or Acoustic Scene Classification to ensure robu…
Adaptive Slimming for Scalable and Efficient Speech Enhancement
Riccardo Miccini, Minje Kim, Clément Laroche +2
Speech enhancement (SE) enables robust speech recognition, real-time communication, hearing aids, and other applications where speech quality is crucial. However, deploying such sy…
Scalable Speech Enhancement with Dynamic Channel Pruning
Riccardo Miccini, Clement Laroche, Tobias Piechowiak +1
Speech Enhancement (SE) is essential for improving productivity in remote collaborative environments. Although deep learning models are highly effective at SE, their computational…
Towards a tailored mixed-precision sub-8-bit quantization scheme for Gated Recurrent Units using Genetic Algorithms
Riccardo Miccini, Alessandro Cerioli, Clément Laroche +3
Despite the recent advances in model compression techniques for deep neural networks, deploying such models on ultra-low-power embedded devices still proves challenging. In particu…
Dynamic nsNet2: Efficient Deep Noise Suppression with Early Exiting
Riccardo Miccini, Alaa Zniber, Clément Laroche +6
Although deep learning has made strides in the field of deep noise suppression, leveraging deep architectures on resource-constrained devices still proved challenging. Therefore, w…