• The Journal of the Convergence on Culture Technology
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• v.6 no.3
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• pp.463-468
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• 2020

Artificial intelligence researches are being applied and developed in various fields. In this paper, we build a neural network by using the method of implementing artificial intelligence in the form of spiking natural networks (SNN), the next-generation of artificial intelligence research, and analyze how the number of neurons in that neural networks affect the performance of the neural networks. We also analyze how the performance of neural networks changes while increasing the amount of neural network learning. The findings will help optimize SNN-based neural networks used in each field.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.05a
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• pp.347-350
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• 2021

본 논문에서는 지도학습(Supervised Learning)알고리즘을 사용하는 모듈러 스파이킹 신경회로망(Modular Spiking Neural Networks)에서 학습의 진행 상황에 맞추어 학습용 데이터를 사용하는 다중 단계 학습알고리즘을 제안한다. 또한 컴퓨터 시뮬레이션에 의하여 항공영상 클러스터링 문제에 적용한 결과를 보임으로써 실제적인 문제에서의 적용 타당성과 가능성을 보인다.

• Smart Media Journal
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• v.11 no.3
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• pp.26-30
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• 2022

Spiking neural network is a neural network that applies the working principle of real brain neurons. Due to the biological mechanism of neurons, it consumes less power for training and reasoning than conventional neural networks. Recently, as deep learning models become huge and operating costs increase exponentially, the spiking neural network is attracting attention as a third-generation neural network that connects convolution neural networks and recurrent neural networks, and related research is being actively conducted. However, in order to apply the spiking neural network model to the industry, a lot of research still needs to be done, and the problem of model retraining to apply a new model must also be solved. In this paper, we propose a method to minimize the cost of model retraining by extracting the weights of the existing trained deep learning model and converting them into the weights of the spiking neural network model. In addition, it was found that weight conversion worked correctly by comparing the results of inference using the converted weights with the results of the existing model.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.2
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• pp.165-170
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• 2020

The spiking neural network operates in a different mechanism than the existing neural network. The existing neural network transfers the output value to the next neuron via an activation function that does not take into account the biological mechanism for the input value to the neuron that makes up the neural network. In addition, there have been good results using deep structures such as VGGNet, ResNet, SSD and YOLO. spiking neural networks, on the other hand, operate more like the biological mechanism of real neurons than the existing activation function, but studies of deep structures using spiking neurons have not been actively conducted compared to in-depth neural networks using conventional neurons. This paper proposes the method of loading an deep neural network model made from existing neurons into a conversion tool and converting it into a spiking deep neural network through the method of replacing an existing neuron with a spiking neuron.

• The Journal of the Convergence on Culture Technology
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• v.8 no.5
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• pp.613-618
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• 2022

Research on artificial intelligence based on SNN (Spiking Neural Networks) is drawing attention as a next-generation artificial intelligence that can overcome the limitations of artificial intelligence based on DNN (Deep Neural Networks) that is currently popular. In this paper, we describe the structure of the SNN compiler, a system SW that generate code from SNN description for neuromorphic computing systems. We also introduce the algorithms used for compiler implementation and present experimental results on how the execution time varies in neuromorphic computing systems depending on the the mapping algorithm. The mapping algorithm proposed in the text showed a performance improvement of up to 3.96 times over a random mapping. The results of this study will allow SNNs to be applied in various neuromorphic hardware.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.11a
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• pp.793-795
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• 2020

본 논문에서는 스파이킹 뉴론(Spiking Neuron)들이 쿨롱에너지 포텐셜 (Coulomb Energy Potential)을 가지는 스파이킹 신경회로망에서의 학습알고리즘을 일반화하여 구간연산(Interval Arithmetic)의 학습이 가능한 학습알고리즘을 제안한다. 제안하는 학습알고리즘은 입력 데이터로서 구간(Interval) 데이터와 포인트(Point) 데이터를 모두 학습 할 수 있는 일반화된 학습알고리즘으로서 간단한 컴퓨터 시뮬레이션을 통하여 범위(Lower bound & Upper bound)를 가지는 구간데이터와 포인트데이터의 통합적인 학습이 가능하고 전문가시스템(expert system)에서의 "don't care attributes"의 학습 등에도 활용이 가능함을 보인다.

• Journal of Sensor Science and Technology
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• v.31 no.1
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• pp.16-23
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• 2022

Cardiac disease is the most common cause of death worldwide. Therefore, detection and classification of electrocardiogram (ECG) signals are crucial to extend life expectancy. In this study, we aimed to implement an artificial intelligence signal recognition system in field programmable gate array (FPGA), which can recognize patterns of bio-signals such as ECG in edge devices that require batteries. Despite the increment in classification accuracy, deep learning models require exorbitant computational resources and power, which makes the mapping of deep neural networks slow and implementation on wearable devices challenging. To overcome these limitations, spiking neural networks (SNNs) have been applied. SNNs are biologically inspired, event-driven neural networks that compute and transfer information using discrete spikes, which require fewer operations and less complex hardware resources. Thus, they are more energy-efficient compared to other artificial neural networks algorithms.

• Journal of Sensor Science and Technology
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• v.30 no.1
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• pp.20-24
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• 2021

The electrocardiogram (ECG) is one of the most extensively employed signals used to diagnose and predict cardiovascular diseases (CVDs). In recent years, several deep learning (DL) models have been proposed to improve detection accuracy. Among these, deep neural networks (DNNs) are the most popular, wherein the features are extracted automatically. Despite the increment in classification accuracy, DL models require exorbitant computational resources and power. This causes the mapping of DNNs to be slow; in addition, the mapping is challenging for a wearable device. Embedded systems have constrained power and memory resources. Therefore full-precision DNNs are not easily deployable on devices. To make the neural network faster and more power-efficient, spiking neural networks (SNNs) have been introduced for fewer operations and less complex hardware resources. However, the conventional SNN has low accuracy and high computational cost. Therefore, this paper proposes a new binarized SNN which modifies the synaptic weights of SNN constraining it to be binary (+1 and -1). In the simulation results, this paper compares the DL models and SNNs and evaluates which model is optimal for ECG classification. Although there is a slight compromise in accuracy, the latter proves to be energy-efficient.

• Smart Media Journal
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• v.11 no.11
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• pp.32-39
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• 2022

As IoT applications that provide AI services increase, various hardware and software that support autonomous learning and inference are being developed. However, as the characteristics and constraints of each hardware increase difficulties in developing IoT applications, the development of an integrated platform is required. In this paper, we propose a tool for automatically generating components based on artificial neural networks and spiking neural networks as well as IoT technologies to be compatible with open platforms. The proposed component automatic generation tool supports the creation of components considering the characteristics of various hardware devices through the virtual component layer of IoT and AI and enables automatic application to open platforms.

• Smart Media Journal
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• v.11 no.2
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• pp.70-76
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• 2022

IoT application development using a cloud server causes problems such as data transmission and reception delay, network traffic, and cost for real-time processing support in network connected hardware. To solve this problem, edge cloud-based platforms can use neuromorphic hardware to enable fast data transfer. In this paper, we propose a model optimization method for supporting spiking neural networks on FPGA hardware. We focused on auto-adjusting network model parameters optimized for neuromorphic hardware. The proposed method performs optimization to show higher performance based on user requirements for accuracy. As a result of performance analysis, it satisfies all requirements of accuracy and showed higher performance in terms of expected execution time, unlike the naive method supported by the existing open source framework.