An FPGA Implementation of Convolutional Spiking Neural Networks for Radioisotope Identification

Huang, Xiaoyu; Jones, Edward; Zhang, Siru; Xie, Shouyu; Furber, Steve; Goulermas, John Y.; Marsden, Edward; Baistow, Ian; Mitra, Srinjoy; Hamilton, Alister

doi:10.1109/iscas51556.2021.9401412

Cited by 6 publications

(5 citation statements)

References 9 publications

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“…This combination is very powerful, as it harnesses the power of a convolutional filter alongside the spiking mechanism of IF or LIF neurons. When using low-power FPGA boards, as in several studies [ 49 , 51 , 52 , 53 , 54 , 55 , 56 ], it is challenging to balance both the deeper convolutions and spiking mechanisms. The current study was able to overcome several of the challenges faced by other works because of the following: We hosted deeper convolutions alongside SNNs with very few parameters compared to [ 49 ] and were still able to achieve similar accuracy over the MNIST and CIFAR10 datasets.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The current study was able to overcome several of the challenges faced by other works because of the following: We hosted deeper convolutions alongside SNNs with very few parameters compared to [ 49 ] and were still able to achieve similar accuracy over the MNIST and CIFAR10 datasets. We employed both real-valued and Poisson distribution spikes as input encoding schemes to capture most of the information before processing them through DCSNNs, which were not used in [ 49 , 51 , 52 , 53 , 54 , 55 , 56 ]. We tested the DCSNNs on automotive relevant datasets such as KITTI, INHA_ADAS, and INHA_KLP as opposed to just MNIST and CIFAR10, as was the case in [ 49 , 52 , 54 , 55 , 56 ].…”

Section: Literature Reviewmentioning

confidence: 99%

“…We employed both real-valued and Poisson distribution spikes as input encoding schemes to capture most of the information before processing them through DCSNNs, which were not used in [ 49 , 51 , 52 , 53 , 54 , 55 , 56 ].…”

Section: Literature Reviewmentioning

confidence: 99%

“…This combination is very powerful, as it harnesses the power of a convolutional filter alongside the spiking mechanism of IF or LIF neurons. When using low-power FPGA boards, as in several studies [49,[51][52][53][54][55][56], it is challenging to balance both the deeper convolutions and spiking mechanisms. The current study was able to overcome several of the challenges faced by other works because of the following:…”

Section: Literature Reviewmentioning

confidence: 99%

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Implementation of Field-Programmable Gate Array Platform for Object Classification Tasks Using Spike-Based Backpropagated Deep Convolutional Spiking Neural Networks

Kakani

Cui

et al. 2023

Micromachines

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This paper investigates the performance of deep convolutional spiking neural networks (DCSNNs) trained using spike-based backpropagation techniques. Specifically, the study examined temporal spike sequence learning via backpropagation (TSSL-BP) and surrogate gradient descent via backpropagation (SGD-BP) as effective techniques for training DCSNNs on the field programmable gate array (FPGA) platform for object classification tasks. The primary objective of this experimental study was twofold: (i) to determine the most effective backpropagation technique, TSSL-BP or SGD-BP, for deeper spiking neural networks (SNNs) with convolution filters across various datasets; and (ii) to assess the feasibility of deploying DCSNNs trained using backpropagation techniques on low-power FPGA for inference, considering potential configuration adjustments and power requirements. The aforementioned objectives will assist in informing researchers and companies in this field regarding the limitations and unique perspectives of deploying DCSNNs on low-power FPGA devices. The study contributions have three main aspects: (i) the design of a low-power FPGA board featuring a deployable DCSNN chip suitable for object classification tasks; (ii) the inference of TSSL-BP and SGD-BP models with novel network architectures on the FPGA board for object classification tasks; and (iii) a comparative evaluation of the selected spike-based backpropagation techniques and the object classification performance of DCSNNs across multiple metrics using both public (MNIST, CIFAR10, KITTI) and private (INHA_ADAS, INHA_KLP) datasets.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

“…This combination is very powerful, as it harnesses the power of a convolutional filter alongside the spiking mechanism of IF or LIF neurons. When using low-power FPGA boards, as in several studies [49,[51][52][53][54][55][56], it is challenging to balance both the deeper convolutions and spiking mechanisms. The current study was able to overcome several of the challenges faced by other works because of the following:…”

Section: Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

Implementation of Field-Programmable Gate Array Platform for Object Classification Tasks Using Spike-Based Backpropagated Deep Convolutional Spiking Neural Networks

Kakani

Cui

et al. 2023

Micromachines

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show abstract

“…Although the necessity of low-power implementation of source detection and identification algorithms has become apparent and recognized, to the best of our knowledge, literature on the topic is scarce. For example, Huang et al, implemented a convolutional spiking neural network based source detection algorithm on a field programmable gate array (FPGA) and achieved the overall power consumption of 75 mW [17]. However, the work lacks thorough benchmarking of the implemented algorithm, and the approach may not be easily applicable to other already existing algorithms.…”

Section: Introductionmentioning

confidence: 99%

An Ensemble Approach to Computationally Efficient Radiological Anomaly Detection and Isotope Identification

Lee

Cooper

Joshi

et al. 2022

IEEE Trans. Nucl. Sci.

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Radiological source search is a challenging task involving detection and identification of weak sources in a constantly changing radiological background. As of now, many radiological source detection algorithms have been proposed; however, their computational complexity, and hence reliance on power intensive processing units inhibit low-power applications of radiological source search systems. In this work, we introduce the anomaly filter (AF) algorithm; a computationally light, yet effective time-series source detection algorithm based on exponential weighted moving average (EWMA) and Poisson deviance statistics. Then, we demonstrate that the proposed algorithm can be used in ensemble with other more computationally intensive source detection and identification algorithms to achieve both increased detection performance and reduced power consumption. The proposed AF algorithm and the ensemble algorithms were thoroughly benchmarked against several existing source detection and identification algorithms. The results show that the AF algorithm outperforms existing conventional source detection algorithms, and the ensemble approach improves the overall performance of existing source detection and isotope identification algorithms. Furthermore, the AF algorithm and the Non-negative Matrix Factorization approach based source identification (NMF-ID) algorithm were combined and implemented on a singleboard microcontroller and the power consumption was measured. This ensemble algorithm reduced the power consumption of the NMF-ID algorithm almost by a factor of 100, while improving the detection performance of the overall system.

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An Efficient Spiking Convolutional Architecture with Compressed Address Event Representation and Adaptive Delay Asynchronous Clocks

Dai,

Lun,

Wang

et al. 2024

2024 IEEE Biomedical Circuits and Systems Conference (BioCAS)

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An FPGA Implementation of Convolutional Spiking Neural Networks for Radioisotope Identification

Cited by 6 publications

References 9 publications

Implementation of Field-Programmable Gate Array Platform for Object Classification Tasks Using Spike-Based Backpropagated Deep Convolutional Spiking Neural Networks

Implementation of Field-Programmable Gate Array Platform for Object Classification Tasks Using Spike-Based Backpropagated Deep Convolutional Spiking Neural Networks

An Ensemble Approach to Computationally Efficient Radiological Anomaly Detection and Isotope Identification

An Efficient Spiking Convolutional Architecture with Compressed Address Event Representation and Adaptive Delay Asynchronous Clocks

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