PLSM: A Parallelized Liquid State Machine for Unintentional Action Detection

Article Type

Research Article

Publication Title

IEEE Transactions on Emerging Topics in Computing

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 a 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 in our repository https://github.com/sadimanna/Parallelized-LSM-for-Unintentional-Action-Recognition.

First Page

474

Last Page

484

DOI

https://10.1109/TETC.2022.3211011

Publication Date

4-1-2023

Comments

Open Access, Green

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