• 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.

• 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.

• Smart Media Journal
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• v.11 no.2
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• pp.77-83
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• 2022

The neuromorphic architecture uses a spiking neural network (SNN) model to derive more accurate results as more spike values are accumulated through inference experiments. When the inference result converges to a specific value, even if the inference experiment is further performed, the change in the result is smaller and power consumption may increase. In particular, in an AI-based IoT environment, power consumption can be a big problem. Therefore, in this paper, we propose a technique to reduce the power consumption of AI-based IoT by reducing the inference time by adjusting the inference image exposure time in the neuromorphic architecture environment. The proposed technique calculates the next inferred image exposure time by reflecting the change in inference accuracy. In addition, the rate of reflection of the change in inference accuracy can be adjusted with a coefficient value, and an optimal coefficient value is found through a comparison experiment of various coefficient values. In the proposed technique, the inference image exposure time corresponding to the target accuracy is greater than that of the linear technique, but the overall power consumption is less than that of the linear technique. As a result of measuring and evaluating the performance of the proposed method, it is confirmed that the inference experiment applying the proposed method can reduce the final exposure time by about 90% compared to the inference experiment applying the linear method.

• Electronics and Telecommunications Trends
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• v.39 no.1
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• pp.106-106
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• 2024

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.5
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• pp.71-77
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• 2021

Recently, there is a demand for efficient program development of an IoT application support frameworks considering heterogeneous hardware characteristics. In addition, the scope of hardware support is expanding with the development of neuromorphic architecture that mimics the human brain to learn on their own and enables autonomous computing. However, most existing IoT IDE(Integrated Development Environment), it is difficult to support AI(Artificial Intelligence) or to support services combined with various hardware such as neuromorphic architectures. In this paper, we design an AI component abstract model that supports the second-generation ANN(Artificial Neural Network) and the third-generation SNN(Spiking Neural Network), and implemented an autonomous IoT IDE based on the proposed model. IoT developers can automatically create AI components through the proposed technique without knowledge of AI and SNN. The proposed technique is flexible in code conversion according to runtime, so development productivity is high. Through experimentation of the proposed method, it was confirmed that the conversion delay time due to the VCL(Virtual Component Layer) may occur, but the difference is not significant.

• Electronics and Telecommunications Trends
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• v.33 no.4
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• pp.14-22
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• 2018

Deep neural networks (DNNs) are widely used in various domains such as speech and image recognition. DNN software frameworks such as Tensorflow and Caffe contributed to the popularity of DNN because of their easy programming environment. In addition, many companies are developing neuromorphic processing units (NPU) such as Tensor Processing Units (TPUs) and Graphical Processing Units (GPUs) to improve the performance of DNN processing. However, there is a large gap between NPUs and DNN software frameworks due to the lack of framework support for various NPUs. A bridge for the gap is a DNN software platform including DNN optimized compilers and DNN libraries. In this paper, we review the technical trends of DNN software platforms.

• Journal of Sensor Science and Technology
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• v.27 no.1
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• pp.64-67
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• 2018

A new CMOS neuron circuit for implementing bistable synapses with spike-timing-dependent plasticity (STDP) properties has been proposed. In neuromorphic systems using STDP properties, the short-term dynamics of the synaptic efficacies are governed by the relative timing of the pre- and post-synaptic spikes, and the efficacies tend asymptotically to either a potentiated state or to a depressed one on long time scales. The proposed circuit consists of a negative shifter, a current starved inverter and a schmitt trigger designed using 0.18um CMOS technology. The simulation result shows that the proposed circuit can reduce the total size of neurons, and the spike energy of the proposed circuit is much less compared to the conventional circuits.

• Ceramist
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• v.22 no.1
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• pp.17-26
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• 2019

The metal-to-insulator transition (MIT) with external stimuli is one of the main issues in correlated oxides. The physical properties are extremely sensitive to band filling, because the MIT is attributed to the strong correlation between electrons in narrow d-band. Since hydrogen is the smallest and lightest element, it is not only likely to doped reversibly in oxides, but also acts as a dopant to provide electrons. The correlated oxides showing MIT are structurally expanded after hydrogenation, and their electrical properties are drastically changed. Researches on this phenomenon have been actively carried out to date. They are of great scientific importance, and the use of this material is very diverse, including the development of next-generation hydrogen sensor, or hydrogen-based neuromorphic devices.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2019.06a
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• pp.262-263
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• 2019

본 논문에서는 뉴로모픽 시스템 구현을 위해 시냅스 소자의 비선형적인 전도도를 고려한 균일 양자화 방식을 제안한다. 소프트웨어로 학습시킨 가중치에 최댓값을 나누는 것으로 정규화를 수행한다. 그 다음, 제안하는 균일 양자화 방식을 수행한다. 양자화를 수행할 때 소자의 제한적인 전도도 레벨을 고려하여 5 부터 25 레벨로 설정하여 실험하였다. 그 결과 MNIST 시험 데이터 세트의 정확도가 10 레벨에서 95.75%로, 소프트웨어의 정확도와 1%미만의 차이를 가진다.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.35 no.3
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• pp.215-222
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• 2022

Artificial neuromorphic devices are considered the key component in realizing energy-efficient and brain-inspired computing systems. For the artificial neuromorphic devices, various material candidates and device architectures have been reported, including two-dimensional materials, metal-oxide semiconductors, organic semiconductors, and halide perovskite materials. In addition to conventional electrical neuromorphic devices, optoelectronic neuromorphic devices, which operate under a light stimulus, have received significant interest due to their potential advantages such as low power consumption, parallel processing, and high bandwidth. This article reviews the recent progress in optoelectronic neuromorphic devices using various active materials such as two-dimensional materials, metal-oxide semiconductors, organic semiconductors, and halide perovskites