Almost everything we hear today about Artificial Intelligence has some connection to Deep Learning.
Detailed Info Dynamic hard pruning of Neural Networks at the edge of the internet Authors:Lorenzo Valerio, Franco Maria Nardini, Andrea Passarella, Raffaele PeregoTitle:Dynamic hard pruning of Neural Networks at the…
Detailed Info Sub-mW Keyword Spotting on an MCU: Analog Binary Feature Extraction and Binary Neural Networks Authors:Gianmarco Cerutti, Renzo Andri, Lukas Cavigelli, Michele Magno, Elisabetta Farella, Luca Benini.Title:Sub-mW Keyword Spotting…
Underspecification and fairness in machine learning (ML) applications have recently become two prominent issues in the ML community. Acoustic scene classification (ASC) applications have so far remained unaffected by this discussion, but are now becoming increasingly used in real-world systems where fairness and reliability are critical aspects.
Deploying deep learning services for time-sensitive and resource-constrained settings such as IoT using edge computing systems is a challenging task that requires dynamic adjustment of inference time. Multi-exit architectures allow deep neural networks to terminate their execution early in order to adhere to tight deadlines at the cost of accuracy.
It is without a doubt that current technologies can greatly aid the process of monitoring and ensuring the health and safety of citizens, especially when seamlessly integrated within the context of smart cities.
The authors are Vandana Rajan, Alessio Brutti, and Andrea Cavallaro and they will present the results in the IEEE International Conference on Acoustics, Speech, & Signal Processing (ICASSP). The conference will take place in Toronto on the 06th until the 11th of June, 2021.