ReRAM Crossbar based Recurrent Neural Network for human activity detection

Long, Yun; Jung, Eui Min; Kung, Jaeha; Mukhopadhyay, Saibal

doi:10.1109/ijcnn.2016.7727299

Cited by 24 publications

(7 citation statements)

References 21 publications

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“…As far as the hardware implementation is concerned, the solution-based adaption of synaptic parameters can be realized with address-event representation (AER) systems (Park et al, 2012) or memristor crossbar arrays (Long et al, 2016; Ielmini, 2018). The random terms in the synaptic rule can be implemented via the emerging stochastic devices such as spintronic device and memristors (Vincent et al, 2015).…”

Section: Discussionmentioning

confidence: 99%

A Swarm Optimization Solver Based on Ferroelectric Spiking Neural Networks

et al. 2019

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As computational models inspired by the biological neural system, spiking neural networks (SNN) continue to demonstrate great potential in the landscape of artificial intelligence, particularly in tasks such as recognition, inference, and learning. While SNN focuses on achieving high-level intelligence of individual creatures, Swarm Intelligence (SI) is another type of bio-inspired models that mimic the collective intelligence of biological swarms, i.e., bird flocks, fish school and ant colonies. SI algorithms provide efficient and practical solutions to many difficult optimization problems through multi-agent metaheuristic search. Bridging these two distinct subfields of artificial intelligence has the potential to harness collective behavior and learning ability of biological systems. In this work, we explore the feasibility of connecting these two models by implementing a generalized SI model on SNN. In the proposed computing paradigm, we use SNNs to represent agents in the swarm and encode problem solutions with the spike firing rate and with spike timing. The coupled neurons communicate and modulate each other's action potentials through event-driven spikes and synchronize their dynamics around the states of optimal solutions. We demonstrate that such an SI-SNN model is capable of efficiently solving optimization problems, such as parameter optimization of continuous functions and a ubiquitous combinatorial optimization problem, namely, the traveling salesman problem with near-optimal solutions. Furthermore, we demonstrate an efficient implementation of such neural dynamics on an emerging hardware platform, namely ferroelectric field-effect transistor (FeFET) based spiking neurons. Such an emerging in-silico neuron is composed of a compact 1T-1FeFET structure with both excitatory and inhibitory inputs. We show that the designed neuromorphic system can serve as an optimization solver with high-performance and high energy-efficiency.

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Section: Discussionmentioning

confidence: 99%

A Swarm Optimization Solver Based on Ferroelectric Spiking Neural Networks

et al. 2019

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“…Initially, the idea of using temporal information was proposed in 1991 [178] to recognize a finger alphabet consisting of 42 symbols and in 1995 [179] to classify 66 different hand shapes with about 98% accuracy. Since then, the recurrent neural network (RNN) with time series as input has been widely applied to classify human activities or estimate hand gestures [180][181][182][183][184][185][186][187].…”

Section: Recurrent Neural Network (Rnn)mentioning

confidence: 99%

Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances

Zhang

et al. 2022

Sensors

249

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Mobile and wearable devices have enabled numerous applications, including activity tracking, wellness monitoring, and human–computer interaction, that measure and improve our daily lives. Many of these applications are made possible by leveraging the rich collection of low-power sensors found in many mobile and wearable devices to perform human activity recognition (HAR). Recently, deep learning has greatly pushed the boundaries of HAR on mobile and wearable devices. This paper systematically categorizes and summarizes existing work that introduces deep learning methods for wearables-based HAR and provides a comprehensive analysis of the current advancements, developing trends, and major challenges. We also present cutting-edge frontiers and future directions for deep learning-based HAR.

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“…Initially, the idea of using temporal information was proposed in 1991 [141] to recognize a finger alphabet consisting of 42 symbols and in 1995 [199] to classify 66 different hand shapes with about 98% accuracy. Since then, the recurrent neural network (RNN) with time series as input has been widely applied to classify human activities or estimate hand gestures [37,43,90,124,189,190,194].…”

Section: Recurrent Neural Network (Rnn)mentioning

confidence: 99%

Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances

Zhang¹,

Li²,

Zhang³

et al. 2021

Preprint

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Mobile and wearable devices have enabled numerous applications, including activity tracking, wellness monitoring, and human-computer interaction, that measure and improve our daily lives. Many of these applications are made possible by leveraging the rich collection of low-power sensors found in many mobile and wearable devices to perform human activity recognition (HAR). Recently, deep learning has greatly pushed the boundaries of HAR on mobile and wearable devices. This paper systematically categorizes and summarizes existing work that introduces deep learning methods for wearables-based HAR and provides a comprehensive analysis of the current advancements, developing trends, and major challenges. We also present cutting-edge frontiers and future directions for deep learning-based HAR.

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ReRAM Crossbar based Recurrent Neural Network for human activity detection

Cited by 24 publications

References 21 publications

A Swarm Optimization Solver Based on Ferroelectric Spiking Neural Networks

A Swarm Optimization Solver Based on Ferroelectric Spiking Neural Networks

Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances

Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances

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