PageRank Implemented with the MPI Paradigm Running on a Many-Core Neuromorphic Platform

Forno, Evelina; Salvato, Alessandro; Macii, Enrico; Urgese, Gianvito

doi:10.3390/jlpea11020025

Cited by 4 publications

(2 citation statements)

References 21 publications

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“…We will also consider spiking input data produced by event-based sensors such as silicon cochleas. As more and more options appear in the field of neuromorphic encoding, benchmarking studies (Stewart et al, 2015 ; Blouw et al, 2019 ; Davies, 2019 ; Forno et al, 2021a ) are on their way to becoming a valuable tool in guiding research and development toward the most suitable solution for any given application.…”

Section: Discussionmentioning

confidence: 99%

Spike encoding techniques for IoT time-varying signals benchmarked on a neuromorphic classification task

et al. 2022

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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 via an equivalent ANN, and model compression schemes aimed at resource optimization. The resulting performance comparison and analysis provides a powerful practical tool, empowering developers to select the most suitable coding method based on the type of data and the desired processing algorithms, and further expands the applicability of neuromorphic computational paradigms to embedded sensor systems widely employed in the IoT and industrial domains.

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Section: Discussionmentioning

confidence: 99%

Spike encoding techniques for IoT time-varying signals benchmarked on a neuromorphic classification task

et al. 2022

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“…In [85,86], authors have investigated the advantages of using the SpiNNaker neuromorphic architecture [34] for executing massively parallel general-purpose algorithms such as PageRank and DNA sequence matching, implemented with the MPI paradigm.…”

Section: Benchmarks and Iot Applications Of Neuromorphic Solutionsmentioning

confidence: 99%

Human activity recognition: suitability of a neuromorphic approach for on-edge AIoT applications

Fra

Forno

Pignari

et al. 2022

Neuromorph. Comput. Eng.

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Human activity recognition (HAR) is a classification problem involving time-dependent signals produced by body monitoring, and its application domain covers all the aspects of human life, from healthcare to sport, from safety to smart environments. As such, it is naturally well suited for on-edge deployment of personalized point-of-care (POC) analyses or other tailored services for the user. However, typical smart and wearable devices suffer from relevant limitations regarding energy consumption, and this significantly hinders the possibility for successful employment of edge computing for tasks like HAR. In this paper, we investigate how this problem can be mitigated by adopting a neuromorphic approach. By comparing optimized classifiers based on traditional deep neural network (DNN) architectures as well as on recent alternatives like the Legendre Memory Unit (LMU), we show how spiking neural networks (SNNs) can effectively deal with the temporal signals typical of HAR providing high performances at a low energy cost. By carrying out an application-oriented hyperparameter optimization, we also propose a methodology flexible to be extended to different domains, to enlarge the field of neuro-inspired classifier suitable for on-edge artificial intelligence of things (AIoT) applications.

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A Lightweight Neuromorphic CNN for Human Activity Recognition on Edge Device

Agarwal

Alam

2023

2023 7th International Conference on Intelligent Computing and Control Systems (ICICCS)

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PageRank Implemented with the MPI Paradigm Running on a Many-Core Neuromorphic Platform

Cited by 4 publications

References 21 publications

Spike encoding techniques for IoT time-varying signals benchmarked on a neuromorphic classification task

Spike encoding techniques for IoT time-varying signals benchmarked on a neuromorphic classification task

Human activity recognition: suitability of a neuromorphic approach for on-edge AIoT applications

A Lightweight Neuromorphic CNN for Human Activity Recognition on Edge Device

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