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Found 8 papers, 1 papers with code
tinyCLAP: Distilling Constrastive Language-Audio Pretrained Models
Contrastive Language-Audio Pretraining (CLAP) became of crucial importance in the field of audio and speech processing.
XiNet: Efficient Neural Networks for tinyML
The recent interest in the edge-to-cloud continuum paradigm has emphasized the need for simple and scalable architectures to deliver optimal performance on computationally constrained devices.
Low-complexity acoustic scene classification in DCASE 2022 Challenge
The provided baseline system is a convolutional neural network which employs post-training quantization of parameters, resulting in 46. 5 K parameters, and 29. 23 million multiply-and-accumulate operations (MMACs).
NEAR - Newborns EEG Artifact Removal
We propose Newborn EEG Artifact Removal (NEAR), a pipeline for EEG artifact removal designed explicitly for human newborns.
PhiNets: a scalable backbone for low-power AI at the edge
In the Internet of Things era, where we see many interconnected and heterogeneous mobile and fixed smart devices, distributing the intelligence from the cloud to the edge has become a necessity.
Enabling energy efficient machine learning on a Ultra-Low-Power vision sensor for IoT
Therefore, domain-specific pipelines are usually delivered in order to exploit the full potential of these cameras.
Sound Event Detection with Binary Neural Networks on Tightly Power-Constrained IoT Devices
This BNN reaches a 77. 9% accuracy, just 7% lower than the full-precision version, with 58 kB (7. 2 times less) for the weights and 262 kB (2. 4 times less) memory in total.
Compact recurrent neural networks for acoustic event detection on low-energy low-complexity platforms
This paper addresses the application of sound event detection at the edge, by optimizing deep learning techniques on resource-constrained embedded platforms for the IoT.
