ETRI Journal最新文献

{"title":"Correction to “NEST-C: A deep learning compiler framework for heterogeneous computing systems with artificial intelligence accelerators”","authors":"Jeman Park,&nbsp;Misun Yu,&nbsp;Jinse Kwon,&nbsp;Junmo Park,&nbsp;Jemin Lee,&nbsp;Yongin Kwon","doi":"10.4218/etr2.12748","DOIUrl":"https://doi.org/10.4218/etr2.12748","url":null,"abstract":"<p>NEST-C: A deep learning compiler framework for heterogeneous computing systems with artificial intelligence accelerators</p><p>https://doi.org/10.4218/etrij.2024-0139</p><p>ETRI Journal, Volume 46, Issue 5, October 2024, pp. 851–864.</p><p>In the article entitled “NEST-C: A deep learning compiler framework for heterogeneous computing systems with artificial intelligence accelerators,” the authors would like to correct the funding information of their article. It should be written as follows:</p><p><b>Funding information</b> This study is supported by a grant from the Institute of Information &amp; Communications Technology Planning &amp; Evaluation (IITP), funded by the Korean government (MSIT) (No. RS-2023-00277060, Development of OpenEdge AI SoC hardware and software platform and No. 2018-0-00769, Neuromorphic Computing Software Platform for Artificial Intelligence Systems).</p><p>The authors would like to apologize for the inconvenience caused.</p>","PeriodicalId":11901,"journal":{"name":"ETRI Journal","volume":"46 6","pages":"1126"},"PeriodicalIF":1.3,"publicationDate":"2024-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.4218/etr2.12748","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142860615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction to “Low-complexity patch projection method for efficient and lightweight point-cloud compression”","authors":"","doi":"10.4218/etr2.12746","DOIUrl":"https://doi.org/10.4218/etr2.12746","url":null,"abstract":"<p><b>Sungryeul Rhyu</b> | <b>Junsik Kim | Gwang Hoon Park | Kyuheon Kim</b></p><p>Low-complexity patch projection method for efficient and lightweight point-cloud compression</p><p>https://doi.org/10.4218/etrij.2023-0242</p><p><i>ETRI Journal</i>, Volume 46, Issue 4, August 2024, pp. 683–696.</p><p>In the article entitled “Low-complexity patch projection method for efficient and lightweight point-cloud compression”, the authors would like to correct the funding information of their article. It should be written as follows:</p><p><b>Funding information</b></p><p>This study was supported by the Information Technology Research Center of the Ministry of Science and ICT, Korea (grant number: IITP-2024-2021-0-02046) and the Institute of Information &amp; Communications Technology Planning &amp; Evaluation, Korea (grant number: RS-2023-00227431, Development of 3D space digital media standard technology).</p><p>The authors would like to apologize for the inconvenience caused.</p>","PeriodicalId":11901,"journal":{"name":"ETRI Journal","volume":"46 6","pages":"1125"},"PeriodicalIF":1.3,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.4218/etr2.12746","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142862152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SNN eXpress: Streamlining Low-Power AI-SoC Development With Unsigned Weight Accumulation Spiking Neural Network","authors":"Hyeonguk Jang,&nbsp;Kyuseung Han,&nbsp;Kwang-Il Oh,&nbsp;Sukho Lee,&nbsp;Jae-Jin Lee,&nbsp;Woojoo Lee","doi":"10.4218/etrij.2024-0114","DOIUrl":"https://doi.org/10.4218/etrij.2024-0114","url":null,"abstract":"<p>SoCs with analog-circuit-based unsigned weight-accumulating spiking neural networks (UWA-SNNs) are a highly promising solution for achieving low-power AI-SoCs. This paper addresses the challenges that must be overcome to realize the potential of UWA-SNNs in low-power AI-SoCs: (i) the absence of UWA-SNN learning methods and the lack of an environment for developing applications based on trained SNN models and (ii) the inherent issue of testing and validating applications on the system being nearly impractical until the final chip is fabricated owing to the mixed-signal circuit implementation of UWA-SNN-based SoCs. This paper argues that, by integrating the proposed solutions, the development of an EDA tool that enables the easy and rapid development of UWA-SNN-based SoCs is feasible, and demonstrates this through the development of the SNN eXpress (SNX) tool. The developed SNX automates the generation of RTL code, FPGA prototypes, and a software development kit tailored for UWA-SNN-based application development. Comprehensive details of SNX development and the performance evaluation and verification results of two AI-SoCs developed using SNX are also presented.</p>","PeriodicalId":11901,"journal":{"name":"ETRI Journal","volume":"46 5","pages":"829-838"},"PeriodicalIF":1.3,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.4218/etrij.2024-0114","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142525391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PF-GEMV: Utilization maximizing architecture in fast matrix–vector multiplication for GPT-2 inference","authors":"Hyeji Kim,&nbsp;Yeongmin Lee,&nbsp;Chun-Gi Lyuh","doi":"10.4218/etrij.2024-0111","DOIUrl":"https://doi.org/10.4218/etrij.2024-0111","url":null,"abstract":"<p>Owing to the widespread advancement of transformer-based artificial neural networks, artificial intelligence (AI) processors are now required to perform matrix–vector multiplication in addition to the conventional matrix–matrix multiplication. However, current AI processor architectures are optimized for general matrix–matrix multiplications (GEMMs), which causes significant throughput degradation when processing general matrix–vector multiplications (GEMVs). In this study, we proposed a port-folding GEMV (PF-GEMV) scheme employing multiformat and low-precision techniques while reusing an outer product-based processor optimized for conventional GEMM operations. This approach achieves 93.7% utilization in GEMV operations with an 8-bit format on an 8 \u0000<span></span><math>\u0000 <mo>×</mo></math> 8 processor, thus resulting in a 7.5 \u0000<span></span><math>\u0000 <mo>×</mo></math> increase in throughput compared with that of the original scheme. Furthermore, when applied to the matrix operation of the GPT-2 large model, an increase in speed by 7 \u0000<span></span><math>\u0000 <mo>×</mo></math> is achieved in single-batch inferences.</p>","PeriodicalId":11901,"journal":{"name":"ETRI Journal","volume":"46 5","pages":"817-828"},"PeriodicalIF":1.3,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.4218/etrij.2024-0111","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142525397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Special issue on next-gen AI and quantum technology","authors":"Ji-Hoon Kim,&nbsp;Ho-Young Cha,&nbsp;Daewoong Kwon,&nbsp;Gyu Sang Choi,&nbsp;HeeSeok Kim,&nbsp;Yousung Kang","doi":"10.4218/etr2.12735","DOIUrl":"https://doi.org/10.4218/etr2.12735","url":null,"abstract":"&lt;p&gt;Artificial intelligence (AI) and quantum technology are two key fields that drive the development of modern science and technology, and their developments have had tremendous impacts on academia and industry. AI is a technology that can solve complex problems through data-based learning and inference and is already driving innovation in various industries such as healthcare, finance, and manufacturing. In particular, the development of AI has enabled practical applications in autonomous driving, natural language processing, and image recognition, greatly improving the quality of human life.&lt;/p&gt;&lt;p&gt;Quantum technology utilizes the principles of quantum mechanics to provide new computational capabilities beyond the scope of classical computing methods. Quantum computing has the potential to perform multiple calculations simultaneously using quantum bits (qubits), which is expected to lead to innovative results in complex optimization problems and the analysis of large datasets. Additionally, quantum technology plays an important role in secure communication, with technologies such as quantum key distribution (QKD) providing security surpassing that of existing encryption methods.&lt;/p&gt;&lt;p&gt;The Electronics and Telecommunications Research Institute (ETRI) Journal is a peer-reviewed open-access journal that launched in 1993 and is published bimonthly by ETRI of the Republic of Korea, aiming to promote worldwide academic exchange in information, telecommunications, and electronics. This special issue of the ETRI Journal focuses on exploring the latest research on these cutting-edge technologies and highlighting the challenges and opportunities that each technology presents. The research included in this special issue clearly demonstrates the significant impact that each of the advancements in both AI and quantum technologies have on academia and industry. AI is already driving change in many fields and is focused on creating more efficient and intelligent systems. In contrast, quantum technologies are introducing a novel computing paradigm, revealing groundbreaking possibilities for computational power and secure communication.&lt;/p&gt;&lt;p&gt;The papers selected for this special issue cover various aspects of AI and quantum technologies. In the AI field, the latest hardware architectures, energy-efficient AI systems such as spiking neural networks (SNNs), and AI application technologies such as anomaly detection are introduced. In the field of quantum technology, theoretical developments in quantum computing, quantum photonic systems, and secure communication technologies such as QKD are discussed.&lt;/p&gt;&lt;p&gt;The first paper [&lt;span&gt;1&lt;/span&gt;], titled “Trends in quantum reinforcement learning: State-of-the-arts and the road ahead by Park and Kim,” is an invited paper. This paper presents the foundational quantum reinforcement learning theory and explores quantum-neural-network-based reinforcement learning models with advantages such as fast training and scalability. It a","PeriodicalId":11901,"journal":{"name":"ETRI Journal","volume":"46 5","pages":"743-747"},"PeriodicalIF":1.3,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.4218/etr2.12735","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142525396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Metaheuristic optimization scheme for quantum kernel classifiers using entanglement-directed graphs","authors":"Yozef Tjandra,&nbsp;Hendrik Santoso Sugiarto","doi":"10.4218/etrij.2024-0144","DOIUrl":"https://doi.org/10.4218/etrij.2024-0144","url":null,"abstract":"<p>Entanglement is crucial for achieving quantum advantages. However, in the context of quantum machine learning, existing optimization strategies for generating quantum classifier circuits often result in unentangled circuits, indicating an underutilization of the entanglement effect needed to learn complex patterns. In this study, we proposed a novel metaheuristic approach—genetic algorithm—for designing a quantum kernel classifier that incorporates expressive entanglement. This classifier utilizes a loopless entanglement-directed graph, where each directed edge represents the entanglement between the target and control qubits. The proposed method consistently outperforms classical and quantum baselines across various artificial and actual datasets, achieving improvements up to 32.4<i>%</i> and 17.5<i>%</i>, respectively, compared with the best model among all other baselines. Moreover, this method successfully reconstructs the hidden entanglement structures underlying artificial datasets. The results also demonstrate that the optimized circuits exhibit diverse entanglement variations across different datasets, indicating the versatility of the proposed approach.</p>","PeriodicalId":11901,"journal":{"name":"ETRI Journal","volume":"46 5","pages":"793-805"},"PeriodicalIF":1.3,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.4218/etrij.2024-0144","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142525390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AONet: Attention network with optional activation for unsupervised video anomaly detection","authors":"Akhrorjon Akhmadjon Ugli Rakhmonov,&nbsp;Barathi Subramanian,&nbsp;Bahar Amirian Varnousefaderani,&nbsp;Jeonghong Kim","doi":"10.4218/etrij.2024-0115","DOIUrl":"https://doi.org/10.4218/etrij.2024-0115","url":null,"abstract":"<p>Anomaly detection in video surveillance is crucial but challenging due to the rarity of irregular events and ambiguity of defining anomalies. We propose a method called AONet that utilizes a spatiotemporal module to extract spatiotemporal features efficiently, as well as a residual autoencoder equipped with an attention network for effective future frame prediction in video anomaly detection. AONet utilizes a novel activation function called OptAF that combines the strengths of the ReLU, leaky ReLU, and sigmoid functions. Furthermore, the proposed method employs a combination of robust loss functions to address various aspects of prediction errors and enhance training effectiveness. The performance of the proposed method is evaluated on three widely used benchmark datasets. The results indicate that the proposed method outperforms existing state-of-the-art methods and demonstrates comparable performance, achieving area under the curve values of 97.0%, 86.9%, and 73.8% on the UCSD Ped2, CUHK Avenue, and ShanghaiTech Campus datasets, respectively. Additionally, the high speed of the proposed method enables its application to real-time tasks.</p>","PeriodicalId":11901,"journal":{"name":"ETRI Journal","volume":"46 5","pages":"890-903"},"PeriodicalIF":1.3,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.4218/etrij.2024-0115","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142525465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"NEST-C: A deep learning compiler framework for heterogeneous computing systems with artificial intelligence accelerators","authors":"Jeman Park,&nbsp;Misun Yu,&nbsp;Jinse Kwon,&nbsp;Junmo Park,&nbsp;Jemin Lee,&nbsp;Yongin Kwon","doi":"10.4218/etrij.2024-0139","DOIUrl":"https://doi.org/10.4218/etrij.2024-0139","url":null,"abstract":"<p>Deep learning (DL) has significantly advanced artificial intelligence (AI); however, frameworks such as PyTorch, ONNX, and TensorFlow are optimized for general-purpose GPUs, leading to inefficiencies on specialized accelerators such as neural processing units (NPUs) and processing-in-memory (PIM) devices. These accelerators are designed to optimize both throughput and energy efficiency but they require more tailored optimizations. To address these limitations, we propose the NEST compiler (NEST-C), a novel DL framework that improves the deployment and performance of models across various AI accelerators. NEST-C leverages profiling-based quantization, dynamic graph partitioning, and multi-level intermediate representation (IR) integration for efficient execution on diverse hardware platforms. Our results show that NEST-C significantly enhances computational efficiency and adaptability across various AI accelerators, achieving higher throughput, lower latency, improved resource utilization, and greater model portability. These benefits contribute to more efficient DL model deployment in modern AI applications.</p>","PeriodicalId":11901,"journal":{"name":"ETRI Journal","volume":"46 5","pages":"851-864"},"PeriodicalIF":1.3,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.4218/etrij.2024-0139","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142525392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Free-space quantum key distribution transmitter system using WDM filter for channel integration","authors":"Minchul Kim,&nbsp;Kyongchun Lim,&nbsp;Joong-Seon Choe,&nbsp;Byung-Seok Choi,&nbsp;Kap-Joong Kim,&nbsp;Ju Hee Baek,&nbsp;Chun Ju Youn","doi":"10.4218/etrij.2024-0142","DOIUrl":"https://doi.org/10.4218/etrij.2024-0142","url":null,"abstract":"<p>In this study, we report a transmitter system for free-space quantum key distribution (QKD) using the BB84 protocol, which does not require an internal alignment process, by using a wavelength-division multiplexing (WDM) filter and polarization-encoding module. With a custom-made WDM filter, the signals required for QKD can be integrated by simply connecting fibers, thus avoiding the laborious internal alignment required for free-space QKD systems using conventional bulk-optic setups. The WDM filter is designed to multiplex the single-mode signals from 785-nm quantum and 1550-nm synchronization channels for spatial-mode matching while maintaining the polarization relations. The measured insertion loss and isolation are 1.8 dB and 32.6 dB for 785 nm and 0.7 dB and 28.3 dB for 1550 nm, respectively. We also evaluate the QKD performance of the proposed system. The sifted key rate and quantum bit error rate are 1.6 Mbps and 0.62%, respectively, at an operating speed of 100 MHz, rendering our system comparable to conventional systems using bulk-optic devices for channel integration.</p>","PeriodicalId":11901,"journal":{"name":"ETRI Journal","volume":"46 5","pages":"806-816"},"PeriodicalIF":1.3,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.4218/etrij.2024-0142","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142525388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fabrication of low-loss symmetrical rib waveguides based on x-cut lithium niobate on insulator for integrated quantum photonics","authors":"Hong-Seok Kim,&nbsp;Guhwan Kim,&nbsp;Tetiana Slusar,&nbsp;Jinwoo Kim,&nbsp;Jiho Park,&nbsp;Jaegyu Park,&nbsp;Hyeon Hwang,&nbsp;Woojin Noh,&nbsp;Hansuek Lee,&nbsp;Min-Kyo Seo,&nbsp;Kiwon Moon,&nbsp;Jung Jin Ju","doi":"10.4218/etrij.2024-0137","DOIUrl":"https://doi.org/10.4218/etrij.2024-0137","url":null,"abstract":"<p>Lithium niobate on insulator (LNOI) is a promising material platform for applications in integrated quantum photonics. A low optical loss is crucial for preserving fragile quantum states. Therefore, in this study, we have fabricated LNOI rib waveguides with a low optical propagation loss of 0.16 dB/cm by optimizing the etching conditions for various parameters. The symmetry and smoothness of the waveguides on \u0000<span></span><math>\u0000 <mi>x</mi></math>-cut LNOI are improved by employing a shallow etching process. The proposed method is expected to facilitate the development of on-chip quantum photonic devices based on LNOI.</p>","PeriodicalId":11901,"journal":{"name":"ETRI Journal","volume":"46 5","pages":"783-792"},"PeriodicalIF":1.3,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.4218/etrij.2024-0137","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}