Neuromorphic MEMS sensor network

Rafaie, Mostafa; Hasan, Mohammad H.; Alsaleem, Fadi

doi:10.1063/1.5081804

Cited by 17 publications

(10 citation statements)

References 22 publications

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“…While previous works have shown the potential of performing simple edge computing using MEMS sensors [43], this approach shows a new generation of intelligent sensors can be produced without using large networks of sensory elements [44]. Such sensors will entirely utilize transience, increasing their speeds.…”

Section: Discussionmentioning

confidence: 95%

Colocalized Sensing and Intelligent Computing in Micro-Sensors

Hasan

Al-Ramini

Abdel‐Rahman

et al. 2020

Sensors

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This work presents an approach to delay-based reservoir computing (RC) at the sensor level without input modulation. It employs a time-multiplexed bias to maintain transience while utilizing either an electrical signal or an environmental signal (such as acceleration) as an unmodulated input signal. The proposed approach enables RC carried out by sufficiently nonlinear sensory elements, as we demonstrate using a single electrostatically actuated microelectromechanical system (MEMS) device. The MEMS sensor can perform colocalized sensing and computing with fewer electronics than traditional RC elements at the RC input (such as analog-to-digital and digital-to-analog converters). The performance of the MEMS RC is evaluated experimentally using a simple classification task, in which the MEMS device differentiates between the profiles of two signal waveforms. The signal waveforms are chosen to be either electrical waveforms or acceleration waveforms. The classification accuracy of the presented MEMS RC scheme is found to be over 99%. Furthermore, the scheme is found to enable flexible virtual node probing rates, allowing for up to 4× slower probing rates, which relaxes the requirements on the system for reservoir signal sampling. Finally, our experiments show a noise-resistance capability for our MEMS RC scheme.

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Section: Discussionmentioning

confidence: 95%

Colocalized Sensing and Intelligent Computing in Micro-Sensors

Hasan

Al-Ramini

Abdel‐Rahman

et al. 2020

Sensors

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“…To overcome the challenges, in previous work, we have identified nonlinearity and hysteresis as essential properties for CTRNNs to perform computing. Thus, we have shown that systems exhibiting these properties, such as a network of coupled bi-stable MEMS devices, are candidates for performing CTRNN computing in an analog fashion [ 12 , 13 ]. However, while that work demonstrated an efficient way to perform CTRNN computing using MEMS devices, it followed a typical machine learning structure that separates the input (sensor) layer from the computing layer ( Figure 3 a).…”

Section: Theory and Methodologymentioning

confidence: 99%

“…In our previous work, we presented the novel use of MEMS electrostatic sensor dynamics with special geometric nonlinearities to naturally solve the continuous-time recurrent neural network (CTRNN) equations [ 12 , 13 ]. In that implementation, there is no need for a digital computer to solve the CTRNN equations.…”

Section: Introductionmentioning

confidence: 99%

“…In that implementation, there is no need for a digital computer to solve the CTRNN equations. As an application, it was shown that the dynamics of eight coupled MEMS devices can be trained to perform a classification and object tracking of a mobile robot application [ 13 ]. In this paper, the concept of MEMS-based CTRNN is expanded to enable the use of almost any type of electrostatic MEMS to perform CTRNN computing.…”

Section: Introductionmentioning

confidence: 99%

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Exploiting Pull-In/Pull-Out Hysteresis in Electrostatic MEMS Sensor Networks to Realize a Novel Sensing Continuous-Time Recurrent Neural Network

et al. 2021

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The goal of this paper is to provide a novel computing approach that can be used to reduce the power consumption, size, and cost of wearable electronics. To achieve this goal, the use of microelectromechanical systems (MEMS) sensors for simultaneous sensing and computing is introduced. Specifically, by enabling sensing and computing locally at the MEMS sensor node and utilizing the usually unwanted pull in/out hysteresis, we may eliminate the need for cloud computing and reduce the use of analog-to-digital converters, sampling circuits, and digital processors. As a proof of concept, we show that a simulation model of a network of three commercially available MEMS accelerometers can classify a train of square and triangular acceleration signals inherently using pull-in and release hysteresis. Furthermore, we develop and fabricate a network with finger arrays of parallel plate actuators to facilitate coupling between MEMS devices in the network using actuating assemblies and biasing assemblies, thus bypassing the previously reported coupling challenge in MEMS neural networks.

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“…In a previous work (26) Alternative means of employing MEMS devices for CTRNN include using MEMS structures with nonlinear geometries, such as MEMS arches, and using internal feedback by using electrical resonance in low parasitic capacitance MEMS devices. For simplicity, this work focuses on using electrostatic MEMS devices, operated in the pull-in/release regime (5) to construct a MEMS CTRNN.…”

Section: Mems-ctrnn Model Architecturementioning

confidence: 99%

Simulation for a Mems-Based CTRNN Ultra-Low Power Implementation of Human Activity Recognition

Emad-ud-din

Hasan

Jafari

et al. 2021

Front. Digit. Health

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This paper presents an energy-efficient classification framework that performs human activity recognition (HAR). Typically, HAR classification tasks require a computational platform that includes a processor and memory along with sensors and their interfaces, all of which consume significant power. The presented framework employs microelectromechanical systems (MEMS) based Continuous Time Recurrent Neural Network (CTRNN) to perform HAR tasks very efficiently. In a real physical implementation, we show that the MEMS-CTRNN nodes can perform computing while consuming power on a nano-watts scale compared to the micro-watts state-of-the-art hardware. We also confirm that this huge power reduction doesn't come at the expense of reduced performance by evaluating its accuracy to classify the highly cited human activity recognition dataset (HAPT). Our simulation results show that the HAR framework that consists of a training module, and a network of MEMS-based CTRNN nodes, provides HAR classification accuracy for the HAPT that is comparable to traditional CTRNN and other Recurrent Neural Network (RNN) implantations. For example, we show that the MEMS-based CTRNN model average accuracy for the worst-case scenario of not using pre-processing techniques, such as quantization, to classify 5 different activities is 77.94% compared to 78.48% using the traditional CTRNN.

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Neuromorphic MEMS sensor network

Cited by 17 publications

References 22 publications

Colocalized Sensing and Intelligent Computing in Micro-Sensors

Colocalized Sensing and Intelligent Computing in Micro-Sensors

Exploiting Pull-In/Pull-Out Hysteresis in Electrostatic MEMS Sensor Networks to Realize a Novel Sensing Continuous-Time Recurrent Neural Network

Simulation for a Mems-Based CTRNN Ultra-Low Power Implementation of Human Activity Recognition

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