ArticleFrontiers in neuroscience2022
Spike encoding techniques for IoT time-varying signals benchmarked on a neuromorphic classification task.
Article in Frontiers in neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- The Smart Inertial Device Data from Human Activities dataset.Scientific data · 2026Article
- Neuromorphic intermediate representation: A unified instruction set for interoperable brain-inspired computing.Nature communications · 2024Article
- Spiking neural networks for physiological and speech signals: a review.Biomedical engineering letters · 2024Review
- Integration of neuromorphic AI in event-driven distributed digitized systems: Concepts and research directions.Frontiers in neuroscience · 2023Article
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5 authors.
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Abstract
Spiking Neural Networks (SNNs), known for their potential to enable low energy consumption and computational cost, can bring significant advantages to the realm of embedded machine learning for edge applications. However, input coming from standard digital sensors must be encoded into spike trains before it can be elaborated with neuromorphic computing technologies. We present here a detailed comparison of available spike encoding techniques for the translation of time-varying signals into the event-based signal domain, tested on two different datasets both acquired through commercially available digital devices: the Free Spoken Digit dataset (FSD), consisting of 8-kHz audio files, and the WISDM dataset, composed of 20-Hz recordings of human activity through mobile and wearable inertial sensors. We propose a complete pipeline to benchmark these encoding techniques by performing time-dependent signal classification through a Spiking Convolutional Neural Network (sCNN), including a signal preprocessing step consisting of a bank of filters inspired by the human cochlea, feature extraction by production of a sonogram, transfer learning
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