Spiking Neural Network for Fourier Transform and Object Detection for Automotive Radar

López-Randulfe, Javier; Duswald, Tobias; Bing, Zhenshan; Knoll, Alois

doi:10.3389/fnbot.2021.688344

Cited by 14 publications

(11 citation statements)

References 31 publications

(36 reference statements)

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“…This study proposes an alternative and novel spike-based FT (S-FT), which is suitable for neuromorphic hardware. This study is a major extension of the work presented in [23]. The main novel aspects are listed below:…”

Section: Introductionmentioning

confidence: 92%

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Time-coded Spiking Fourier Transform in Neuromorphic Hardware

López-Randulfe,

Reeb,

Karimi

et al. 2022

Preprint

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After several decades of continuously optimizing computing systems, the Moore's law is reaching its end. However, there is an increasing demand for fast and efficient processing systems that can handle large streams of data while decreasing system footprints. Neuromorphic computing answers this need by creating decentralized architectures that communicate with binary events over time. Despite its rapid growth in the last few years, novel algorithms are needed that can leverage the potential of this emerging computing paradigm and can stimulate the design of advanced neuromorphic chips. In this work, we propose a time-based spiking neural network that is mathematically equivalent to the Fourier transform. We implemented the network in the neuromorphic chip Loihi and conducted experiments on five different real scenarios with an automotive frequency modulated continuous wave radar. Experimental results validate the algorithm, and we hope they prompt the design of ad hoc neuromorphic chips that can improve the efficiency of state-of-the-art digital signal processors and encourage research on neuromorphic computing for signal processing.

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Section: Introductionmentioning

confidence: 92%

“…We exploited this property for reproducing the linear combinations of an FT that operates in more than one dimension, which is explained in more detail in [23].…”

Section: Network Architecturementioning

confidence: 99%

Time-coded Spiking Fourier Transform in Neuromorphic Hardware

López-Randulfe,

Reeb,

Karimi

et al. 2022

Preprint

Self Cite

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“…Grzesiack and Meganck, have developed a mathematical formulation for carrying out spike signal processing and employ it in control systems and linear dynamical systems [89]. Some of the core signal processing algorithms such as Fourier transforms, have been demonstrated with SNN, and have been employed as a pre-processing step in object detection applications [92]. Orchard et al, have demonstrated performing equivalent of short time Fourier transforms, with resonate and fire (RF) neuron models, where different output neurons become active for different frequency components in the input signal [93].…”

Section: Current Neuromorphic Approaches For Signal Processingmentioning

confidence: 99%

“…Few works have also explored spike signal processing from the point of view of novel materials' research, e.g., Ganguly et al, have explored the use of a stochastic analog neuron based on spintronic materials in signal processing tasks such as channel equalization [95]. Neuromorphic approaches are also increasingly being used in assisted and autonomous driving applications to pre-process signals acquired from RADAR and LiDAR sensors [92,[96][97][98]. A great opportunity exists to take inspiration from the signal processing mechanisms in biology such as echolocation to design neuromorphic systems that are highly performant and accurate.…”

Section: Signal Processing Application Domains and Opportunitiesmentioning

confidence: 99%

A review of non-cognitive applications for neuromorphic computing

Aimone

Date

Guerra

et al. 2022

Neuromorph. Comput. Eng.

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Though neuromorphic computers have typically targeted applications in machine learning and neuroscience (``cognitive'' applications), they have many computational characteristics that are attractive for a wide variety of computational problems. In this work, we review the current state-of-the-art for non-cognitive applications on neuromorphic computers, including simple computational kernels for composition, graph algorithms, constrained optimization, and signal processing. We discuss the advantages of using neuromorphic computers for these different applications, as well as the challenges that still remain. The ultimate goal of this work is to bring awareness to this class of problems for neuromorphic systems to the broader community, particularly to encourage further work in this area and to make sure that these applications are considered in the design of future neuromorphic systems.

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“…Synthetic aperture radar (SAR) is a high-resolution microwave imaging and detection system (Xie et al, 2016(Xie et al, , 2017(Xie et al, , 2020Zhang et al, 2022), which has the advantage of all-weather, all-day, and harsh environment work; thus, it can observe the land and ocean in real time for a long time (López-Randulfe et al, 2021;Yu et al, 2022). In recent years, related technology research on ship identification using SAR images has received great attention in the field of marine remote sensing (Lang et al, 2022;Wang et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

High-efficiency and low-energy ship recognition strategy based on spiking neural network in SAR images

Xie

Jiang

et al. 2022

Front. Neurorobot.

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Ship recognition using synthetic aperture radar (SAR) images has important applications in the military and civilian fields. Aiming at the problems of the many model parameters and high-energy losses in the traditional deep learning methods for the target recognition in the SAR images, this study has proposed a high-efficiency and low-energy ship recognition strategy based on the spiking neural network (SNN) in the SAR images. First, the visual attention mechanism is used to extract the visual saliency map from the SAR image, and then the Poisson encoder is used to encode it into a spike train, which can suppress the background noise while retaining the visual saliency feature of the SAR image. Besides, an SNN model integrating the time-series information is constructed by combining the leaked and integrated firing spiking neurons with the convolutional neural network (CNN), which can use the firing frequency of the spiking neurons to realize the ship recognition in SAR images. Finally, to solve the problem that SNN model is difficult to train, the arctangent function is used as the surrogate gradient function of the spike emission function during the backpropagation. Hence, applying this backpropagation method to the training process can optimize the SNN model. The experimental results show the following: (1) the proposed strategy can more accurately recognize the ship in the SAR image, and the F1 score can reach 98.55%, which has a better recognition performance than the other traditional deep learning methods; (2) the proposed strategy has the least amount of model parameters (only 3.11MB), which is far less than the model parameters of the other traditional deep learning methods; (3) the proposed strategy has fewer operations (only 17.97M) and can reach 1/30 time of operands of the other traditional deep learning methods, which shows the high efficiency of the proposed strategy using the spike emission signals; (4) the proposed strategy has the energy loss of 1.38 × 10−6J, which can achieve the low energy advantage of nearly three orders of the magnitude compared to the other traditional deep learning methods, indicating that the proposed strategy has a significant energy efficiency.

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Spiking Neural Network for Fourier Transform and Object Detection for Automotive Radar

Cited by 14 publications

References 31 publications

Time-coded Spiking Fourier Transform in Neuromorphic Hardware

Time-coded Spiking Fourier Transform in Neuromorphic Hardware

A review of non-cognitive applications for neuromorphic computing

High-efficiency and low-energy ship recognition strategy based on spiking neural network in SAR images

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