PLSM: A Parallelized Liquid State Machine for Unintentional Action Detection

• URL: http://arxiv.org/abs/2105.09909v1
• Date: Thu, 6 May 2021 08:10:35 GMT
• Title: PLSM: A Parallelized Liquid State Machine for Unintentional Action Detection
• Authors: Dipayan Das, Saumik Bhattacharya, Umapada Pal, and Sukalpa Chanda
• Abstract summary: Reservoir Computing (RC) offers a viable option to AI algorithms on low-end embedded system platforms. Liquid State Machine (LSM) is a bio-inspired RC model that mimics the cortical neuromorphic spiking neural networks (SNN) that can be directly realized on hardware.
• Score: 14.873546762084063
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Reservoir Computing (RC) offers a viable option to deploy AI algorithms on low-end embedded system platforms. Liquid State Machine (LSM) is a bio-inspired RC model that mimics the cortical microcircuits and uses spiking neural networks (SNN) that can be directly realized on neuromorphic hardware. In this paper, we present a novel Parallelized LSM (PLSM) architecture that incorporates spatio-temporal read-out layer and semantic constraints on model output. To the best of our knowledge, such a formulation has been done for the first time in literature, and it offers a computationally lighter alternative to traditional deep-learning models. Additionally, we also present a comprehensive algorithm for the implementation of parallelizable SNNs and LSMs that are GPU-compatible. We implement the PLSM model to classify unintentional/accidental video clips, using the Oops dataset. From the experimental results on detecting unintentional action in video, it can be observed that our proposed model outperforms a self-supervised model and a fully supervised traditional deep learning model. All the implemented codes can be found at our repository https://github.com/anonymoussentience2020/Parallelized_LSM_for_Unintentional_Action_Recognition.

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