• 전자통신동향분석
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• 제38권6호
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• pp.62-74
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• 2023

In response to diverse external stimuli, sensory receptors generate spiking nerve signals. These generated signals are transmitted to the brain along the neural pathway to advance to the stage of recognition or perception, and then they reach the area of discrimination or judgment for remembering, assessing, and processing incoming information. We review research trends in neuromorphic sensory perception technology inspired by biological sensory perception functions. Among the various senses, we consider sensory nerve decoding technology based on sensory nerve pathways focusing on touch and smell, neuromorphic synapse elements that mimic biological neurons and synapses, and neuromorphic processors. Neuromorphic sensory devices, neuromorphic synapses, and artificial sensory memory devices that integrate storage components are being actively studied. However, various problems remain to be solved, such as learning methods to implement cognitive functions beyond simple detection. Considering applications such as virtual reality, medical welfare, neuroscience, and cranial nerve interfaces, neuromorphic sensory recognition technology is expected to be actively developed based on new technologies, including combinatorial neurocognitive cell technology.

• 전자공학회지
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• 제44권6호
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• pp.50-62
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• 2017

• E2M - 전기 전자와 첨단 소재
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• 제30권8호
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• pp.14-24
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• 2017

• E2M - 전기 전자와 첨단 소재
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• 제34권5호
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• pp.15-23
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• 2021

• E2M - 전기 전자와 첨단 소재
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• 제34권5호
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• pp.33-39
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• 2021

• 인포메이션 디스플레이
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• 제23권5호
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• pp.24-32
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• 2022

• E2M - 전기 전자와 첨단 소재
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• 제37권2호
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• pp.4-11
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• 2024

• 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.

• 전자통신동향분석
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• 제35권4호
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• pp.34-41
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• 2020

The existing Von Neumann architecture places limits to data processing in AI, a booming technology. To address this issue, research is being conducted on computing architectures and artificial neural networks that simulate neurons and synapses, which are the hardware of the human brain. With high-speed, high-throughput data communication infrastructures, photonic solutions today are a mature industrial reality. In particular, due to the recent outstanding achievements of artificial neural networks, there is considerable interest in improving their speed and energy efficiency by exploiting photonic-based neuromorphic hardware instead of electronic-based hardware. This paper covers recent photonic neuromorphic studies and a classification of existing solutions (categorized into multilayer perceptrons, convolutional neural networks, spiking neural networks, and reservoir computing).

• 한국전자통신학회논문지
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• 제13권5호
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• pp.1037-1042
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• 2018

최근 인간의 뇌를 모방하여 정보를 학습하고 처리하는 뉴로모픽 기술에 대한 연구는 꾸준히 진행되고 있다. 뉴로모픽 시스템의 하드웨어 구현은 다수의 간단한 연산절차와 고도의 병렬처리 구조로 구성이 가능하여, 처리속도, 전력소비, 저 복잡도 구현 측면에서 상당한 이점을 가진다. 또한 저 전력, 소형 임베디드 시스템에 적용 가능한 뉴로모픽 기술에 대한 연구가 급증하고 있으며, 정확도 손실 없이 저 복잡도 구현을 위해서는 입력데이터의 차원축소 기술이 필수적이다. 본 논문은 멀티모달 센서 데이터를 처리하기 위해 멀티모달 센서 시스템, 다수의 뉴론 엔진, 뉴론 엔진 컨트롤러 등으로 구성된 경량 인공지능 엔진과 특징추출기를 설계 하였으며, 이를 위한 병렬 뉴론 엔진 구조를 제안하였다. 설계한 인공지능 엔진, 특징 추출기, Micro Controller Unit(MCU)를 연동하여 제안한 경량 인공지능 엔진의 성능 검증을 진행하였다.