Zobrazeno 1 - 10
of 69 761
pro vyhledávání: '"Ultra low-power"'
Speech enhancement is critical for improving speech intelligibility and quality in various audio devices. In recent years, deep learning-based methods have significantly improved speech enhancement performance, but they often come with a high computa
Externí odkaz:
http://arxiv.org/abs/2410.04785
Clock generation is an essential part of wireless or wireline communication modules. To facilitate recent advancements in wireline-like communication and in-sensor computing modules at relatively lower data rates, ultra-low power, and accurate clock
Externí odkaz:
http://arxiv.org/abs/2410.16310
Autor:
Mei, Lan, Ingolfsson, Thorir Mar, Cioflan, Cristian, Kartsch, Victor, Cossettini, Andrea, Wang, Xiaying, Benini, Luca
Driven by the progress in efficient embedded processing, there is an accelerating trend toward running machine learning models directly on wearable Brain-Machine Interfaces (BMIs) to improve portability and privacy and maximize battery life. However,
Externí odkaz:
http://arxiv.org/abs/2409.10654
This paper proposes a self-learning framework to incrementally train (fine-tune) a personalized Keyword Spotting (KWS) model after the deployment on ultra-low power smart audio sensors. We address the fundamental problem of the absence of labeled tra
Externí odkaz:
http://arxiv.org/abs/2408.12481
This paper introduces a new machine learning-assisted chromatic dispersion compensation filter, demonstrating its superior power efficiency compared to conventional FFT-based filters for metro link distances. Validations on FPGA confirmed an energy e
Externí odkaz:
http://arxiv.org/abs/2409.13381
Autor:
Schulthess, Lukas, Marty, Steven, Dirodi, Matilde, Rocha, Mariana D., Rüttimann, Linus, Hahnloser, Richard H. R., Magno, Michele
Animal vocalisations serve a wide range of vital functions. Although it is possible to record animal vocalisations with external microphones, more insights are gained from miniature sensors mounted directly on animals' backs. We present TinyBird-ML;
Externí odkaz:
http://arxiv.org/abs/2407.21486
Autor:
Daghero, Francesco, Burrello, Alessio, Poncino, Massimo, Macii, Enrico, Pagliari, Daniele Jahier
Depthwise separable convolutions are a fundamental component in efficient Deep Neural Networks, as they reduce the number of parameters and operations compared to traditional convolutions while maintaining comparable accuracy. However, their low data
Externí odkaz:
http://arxiv.org/abs/2406.12478