A novel and efficient classifier using spiking neural network

R, Joshua Arul Kumar; Sigamani, Titus; Janet, B.

doi:10.1007/s11227-019-02881-y

Cited by 19 publications

(5 citation statements)

References 12 publications

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“…Deep SNNs were shown to perform well for various perception tasks. 24 By performing ANN to SNN transfer learning, we show that SNNs require a high spiking rate to approximate traditional ANN performance. Spiking rate is correlated with increased energy expenditure, implying a non-energy-efficient utilization of their capacity.…”

Section: Llmentioning

confidence: 92%

Data-driven artificial and spiking neural networks for inverse kinematics in neurorobotics

et al. 2022

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Summary Inverse kinematics is fundamental for computational motion planning. It is used to derive an appropriate state in a robot's configuration space, given a target position in task space. In this work, we investigate the performance of fully connected and residual artificial neural networks as well as recurrent, learning-based, and deep spiking neural networks for conventional and geometrically constrained inverse kinematics. We show that while highly parameterized data-driven neural networks with tens to hundreds of thousands of parameters exhibit sub-ms inference time and sub-mm accuracy, learning-based spiking architectures can provide reasonably good results with merely a few thousand neurons. Moreover, we show that spiking neural networks can perform well in geometrically constrained task space, even when configured to an energy-conserved spiking rate, demonstrating their robustness. Neural networks were evaluated on NVIDIA's Xavier and Intel's neuromorphic Loihi chip.

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Section: Llmentioning

confidence: 92%

Data-driven artificial and spiking neural networks for inverse kinematics in neurorobotics

et al. 2022

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“…Figs. 16 and 17 show the velocity profile during braking in simulation and experimentation phases respectively. It can be seen that a similar braking time for the same number of iterations was obtained in both cases.…”

Section: Experimentationmentioning

confidence: 99%

On-Line Learning Applied to Spiking Neural Network for Antilock Braking Systems

Fernández

Alcázar

Sánchez

et al. 2023

Preprint

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“…Nengo offers a deep learning simulator NengoDL [54] that allows easy integration of TensorFlow library providing access to rich features such as convolution connections. Nengo uses a neural engineering framework (NEF) to design spiking neuron models for applications in machine learning and deep learning, for example, image classification [55], inductive reasoning [56], action selection [56], speech production [57], motor control [58], and planning with problem-solving [59].…”

Section: A Neural Engineering Object (Nengo) Simulatormentioning

confidence: 99%

RadarSNN: A Resource Efficient Gesture Sensing System Based on mm-Wave Radar

Arsalan

Santra

Issakov

2022

IEEE Trans. Microwave Theory Techn.

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Radar offers a promising modality for enabling gesture recognition, which is a simple and intuitive alternative to click and touch-based human-computer interface. In this article, we propose a spiking neural network (SNN)-based hand gesture recognition with frequency-modulated continuous-wave 60-GHz radar. As preprocessing, the 2-D fast Fourier transform (FFT) is performed across fast time and slow time to generate a video of range-Doppler maps, which are then processed to generate range spectrograms, Doppler spectrograms, and angle spectrograms.The spike trains are fed into an SNN to classify the gesture that has been performed. We demonstrate that with few neurons, SNNs can achieve recognition accuracies close to 99.50% comparable to their deep learning counterparts for eight dynamic gestures. Moreover, the proposed model size is 75 kB, which is substantially smaller compared to the state-of-the-art models making it memory efficient. We also demonstrate using tSNE plots that SNNs can operate with lower embedding dimensions, implying that we can realize SNN with a small compute and memory footprint.

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A novel and efficient classifier using spiking neural network

Cited by 19 publications

References 12 publications

Data-driven artificial and spiking neural networks for inverse kinematics in neurorobotics

Data-driven artificial and spiking neural networks for inverse kinematics in neurorobotics

On-Line Learning Applied to Spiking Neural Network for Antilock Braking Systems

RadarSNN: A Resource Efficient Gesture Sensing System Based on mm-Wave Radar

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