• Journal of Multimedia Information System
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• 제3권4호
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• pp.155-160
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• 2016

The Pulse Coupled Neural Network (PCNN) is a kind of neural network models that consists of spiking neurons and local connections. The PCNN was originally proposed as a model that can reproduce the synchronous phenomena of the neurons in the cat visual cortex. Recently, the PCNN has been applied to the various image processing applications, e.g., image segmentation, edge detection, pattern recognition, and so on. The method for the image matching using the PCNN had been proposed as one of the valuable applications of the PCNN. In this method, the Genetic Algorithm is applied to the PCNN parameter learning for the image matching. In this study, we propose the method of the similar image rating using the PCNN. In our method, the Genetic Algorithm based method is applied to the parameter learning of the PCNN. We show the performance of our method by simulations. From the simulation results, we evaluate the efficiency and the general versatility of our parameter learning method.

• 스마트미디어저널
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• 제12권4호
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• pp.19-26
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• 2023

뉴로모픽 아키텍처 기반 하드웨어를 이용한 IoT 엣지 서비스는 단말 장치에서 지능형 처리를 수행할 수 있기 때문에 자율형 IoT 응용 지원에 적합하다. 그러나 IoT 개발자가 뉴로모픽 하드웨어에서 사용되는 SNN을 이해하기에는 어려움이 있다. 본 논문에서는 뉴로모픽 하드웨어의 제약조건을 고려하며 사용자의 요구 성능을 만족하는 SNN 모델 생성 기법을 제안한다. 제안 기법은 프로파일링된 데이터에서 최적의 SNN 모델 파라미터를 찾도록 전처리된 데이터로 사전 학습한 모델을 활용한다. 전체 탐색 기법과 비교 결과, 두 기법 모두 사용자 요구사항을 모두 만족하였지만, 제안 기법이 수행 시간 측면에서 더 좋은 성능을 보였다. 또한, 신규 하드웨어의 제약조건을 명확히 알지 못하더라도 새로운 하드웨어의 프로파일링된 데이터를 활용할 수 있으므로 높은 확장성을 제공할 수 있다.

• 전자통신동향분석
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• 제33권6호
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• pp.24-33
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• 2018

In the age of IoT, in which everything is connected to a network, there have been increases in the amount of data traffic, latency, and the risk of personal privacy breaches that conventional cloud computing technology cannot cope with. The idea of edge computing has emerged as a solution to these issues, and furthermore, the concept of ultra-low power edge intelligent semiconductors in which the IoT device itself performs intelligent decisions and processes data has been established. The key elements of this function are an intelligent semiconductor based on artificial intelligence, connectivity for the efficient connection of neurons and synapses, and a large-scale spiking neural network simulation framework for the performance prediction of a neural network. This paper covers the current trends in ultra-low power edge intelligent semiconductors including issues regarding their technology and application.

• The Korean Journal of Physiology and Pharmacology
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• 제7권6호
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• pp.317-323
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• 2003

Interneuron diversity is one of the key factors to hinder understanding the mechanism of cortical neural network functions even with their important roles. We characterized inhibitory interneurons in layer II/III of the rat primary visual cortex, using patch-clamp recording and confocal reconstruction, and classified inhibitory interneurons into fast spiking (FS), late spiking (LS), burst spiking (BS), and regular spiking non-pyramidal (RSNP) neurons according to their electrophysiological characteristics. Global parameters to identify inhibitory interneurons were resting membrane potential (＞-70 mV) and action potential (AP) width (＜0.9 msec at half amplitude). FS could be differentiated from LS, based on smaller amplitude of the AP (＜∼50 mV) and shorter peak-to-trough time (P-T time) of the afterhyperpolarization (＜4 msec). In addition to the shorter AP width, RSNP had the higher input resistance (＞200 $M{Omega}$) and the shorter P-T time (＜20 msec) than those of regular spiking pyramidal neurons. Confocal reconstruction of recorded cells revealed characteristic morphology of each subtype of inhibitory interneurons. Thus, our results provide at least four subtypes of inhibitory interneurons in layer II/III of the rat primary visual cortex and a classification scheme of inhibitory interneurons.

• Nuclear Engineering and Technology
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• 제55권9호
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• pp.3359-3366
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• 2023

In this study, a spiking cortical model (SCM) based n-g discrimination method is proposed. The SCM-based algorithm is compared with three other methods, namely: (i) the pulse-coupled neural network (PCNN), (ii) the charge comparison, and (iii) the zero-crossing. The objective evaluation criteria used for the comparison are the FoM-value and the time consumption of discrimination. Experimental results demonstrated that our proposed method outperforms the other methods significantly with the highest FoM-value. Specifically, the proposed method exhibits a 34.81% improvement compared with the PCNN, a 50.29% improvement compared with the charge comparison, and a 110.02% improvement compared with the zero-crossing. Additionally, the proposed method features the second-fastest discrimination time, where it is 75.67% faster than the PCNN, 70.65% faster than the charge comparison and 38.4% slower than the zero-crossing. Our study also discusses the role and change pattern of each parameter of the SCM to guide the selection process. It concludes that the SCM's outstanding ability to recognize the dynamic information in the pulse signal, improved accuracy when compared to the PCNN, and better computational complexity enables the SCM to exhibit excellent n-γ discrimination performance while consuming less time.

• 한국인터넷방송통신학회논문지
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• 제21권5호
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• pp.71-77
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• 2021

최근 이질적인 하드웨어 특성을 고려한 IoT 응용 지원 프레임워크의 효율적인 프로그램 개발이 요구되고 있다. 또한, 인간의 뇌를 모사하여 스스로 학습 및 자율적 컴퓨팅이 가능한 뉴로모픽 아키텍처의 발전으로 하드웨어 지원의 범위가 넓어지고 있다. 하지만 기존 대부분의 IoT 통합개발환경에서는 AI(Artificial Intelligence) 기능을 지원하거나 뉴로모픽 아키텍처와 같은 다양한 하드웨어와 결합된 서비스 지원이 어렵다. 본 논문에서는 2세대 인공 신경망 및 3세대 스파이킹 신경망 모델을 모두 지원하는 AI 컴포넌트 추상화 모델을 설계하고 제안 모델 기반의 자율형 IoT 통합개발환경을 구현하였다. IoT 개발자는 AI 및 스파이킹 신경망에 대한 지식이 없어도 제안 기법을 통해 자동으로 AI 컴포넌트를 생성할 수 있으며 런타임에 따라 코드 변환이 유연하여 개발 생산성이 높다. 제안 기법의 실험을 진행하여 가상 컴포넌트 계층으로 인한 변환 지연시간이 발생할 수 있으나 차이가 크지 않음을 확인하였다.

• 센서학회지
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• 제29권1호
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• pp.19-26
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• 2020

Electrocardiogram (ECG) classification has become an essential task of modern day wearable devices, and can be used to detect cardiovascular diseases. State-of-the-art Artificial Intelligence (AI)-based ECG classifiers have been designed using various artificial neural networks (ANNs). Despite their high accuracy, ANNs require significant computational resources and power. Herein, three different ANNs have been compared: multilayer perceptron (MLP), convolutional neural network (CNN), and spiking neural network (SNN) only for the ECG classification. The ANN model has been developed in Python and Theano, trained on a central processing unit (CPU) platform, and deployed on a PYNQ-Z2 FPGA board to validate the model using a Jupyter notebook. Meanwhile, the hardware accelerator is designed with Overlay, which is a hardware library on PYNQ. For classification, the MIT-BIH dataset obtained from the Physionet library is used. The resulting ANN system can accurately classify four ECG types: normal, atrial premature contraction, left bundle branch block, and premature ventricular contraction. The performance of the ECG classifier models is evaluated based on accuracy and power. Among the three AI algorithms, the SNN requires the lowest power consumption of 0.226 W on-chip, followed by MLP (1.677 W), and CNN (2.266 W). However, the highest accuracy is achieved by the CNN (95%), followed by MLP (76%) and SNN (90%).

• 전자통신동향분석
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• 제35권3호
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• pp.76-84
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• 2020

Neuromorphic hardware refers to brain-inspired computers or components that model an artificial neural network comprising densely connected parallel neurons and synapses. The major element in the widespread deployment of neural networks in embedded devices are efficient architecture for neuromorphic hardware with regard to performance, power consumption, and chip area. Spiking neural networks (SiNNs) are brain-inspired in which the communication among neurons is modeled in the form of spikes. Owing to brainlike operating modes, SNNs can be power efficient. However, issues still exist with research and actual application of SNNs. In this issue, we focus on the technology development cases and market trends of two typical tracks, which are listed above, from the point of view of artificial intelligence neuromorphic circuits and subsequently describe their future development prospects.

• 스마트미디어저널
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• 제11권2호
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• pp.77-83
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• 2022

뉴로모픽 아키텍처는 스파이킹 신경망(SNN, Spiking Neural Network) 모델을 사용하여, 추론 실험을 통해 스파이크 값이 많이 누적될수록 정확한 결과를 도출한다. 추론 결과가 특정 값으로 수렴할 경우, 추론 실험을 더 진행해도 결과의 변화가 작아 소비 전력이 더 커질 수 있다. 특히, 인공지능 기반 IoT 환경에서는 전력 낭비는 큰 문제가 될 수 있다. 따라서 본 논문에서는 뉴로모픽 아키텍처 환경에서 추론 이미지 노출 시간을 조절하여 추론 시간을 단축함으로써 인공지능 기반 IoT의 전력 낭비를 줄이는 기법을 제안한다. 제안한 기법은 추론 정확도의 변화를 반영하여 다음 추론 이미지 노출 시간을 계산한다. 또한, 추론 정확도의 변화량 반영비율을 계수 값으로 조절할 수 있으며, 다양한 계수 값의 비교 실험을 통해 최적의 계수 값을 찾는다. 제안한 기법은 목표 정확도에 해당하는 추론 이미지 노출 시간은 선형 기법보다 크지만 최종 추론 시간은 선형 기법보다 적다. 제안한 기법의 성능을 측정하고 평가한 결과, 제안한 기법을 적용한 추론 실험이 선형 기법을 적용한 추론 실험보다 최종 노출 시간을 약 90% 단축할 수 있음을 확인한다.

• 전자통신동향분석
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• 제36권3호
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• pp.106-118
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• 2021

The field of brain science (or neuroscience in a broader sense) has inspired researchers in artificial intelligence (AI) for a long time. The outcomes of neuroscience such as Hebb's rule had profound effects on the early AI models, and the models have developed to become the current state-of-the-art artificial neural networks. However, the recent progress in AI led by deep learning architectures is mainly due to elaborate mathematical methods and the rapid growth of computing power rather than neuroscientific inspiration. Meanwhile, major limitations such as opacity, lack of common sense, narrowness, and brittleness have not been thoroughly resolved. To address those problems, many AI researchers turn their attention to neuroscience to get insights and inspirations again. Biologically plausible neural networks, spiking neural networks, and connectome-based networks exemplify such neuroscience-inspired approaches. In addition, the more recent field of brain network analysis is unveiling complex brain mechanisms by handling the brain as dynamic graph models. We argue that the progress toward the human-level AI, which is the goal of AI, can be accelerated by leveraging the novel findings of the human brain network.