ETRI Journal最新文献

{"title":"Special issue on next-gen AI and quantum technology","authors":"Ji-Hoon Kim, Ho-Young Cha, Daewoong Kwon, Gyu Sang Choi, HeeSeok Kim, Yousung Kang","doi":"10.4218/etr2.12735","DOIUrl":"https://doi.org/10.4218/etr2.12735","url":null,"abstract":"<p>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.</p><p>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.</p><p>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.</p><p>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.</p><p>The first paper [<span>1</span>], 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}

{"title":"Optimal execution of logical Hadamard with low-space overhead in rotated surface code","authors":"Sang-Min Lee,&nbsp;Ki-Sung Jin,&nbsp;Soo-Cheol Oh,&nbsp;Jin-Ho On,&nbsp;Gyu-Il Cha","doi":"10.4218/etrij.2024-0129","DOIUrl":"https://doi.org/10.4218/etrij.2024-0129","url":null,"abstract":"<p>Fault-tolerant quantum computation requires error-correcting codes that enable reliable universal quantum operations. This study introduces a novel approach that executes the logical Hadamard with low-space requirements while preserving the original definition of logical operators within the framework of the rotated surface codes. Our method leverages a boundary deformation method to rotate the logical qubit transformed by transversal Hadamard. Following this, the original encoding of the logical qubit is reinstated through logical flip-and-shift operations. The estimated space–time cost for a logical Hadamard operation with a code distance d is 5<i>d</i>² + 3<i>d</i>². The efficiency enhancement of the proposed method is approximately four times greater than those of previous approaches, regardless of the code distance. Unlike the traditional method, implementing a logical Hadamard requires only two patches instead of seven. Furthermore, the proposed method ensures the parallelism of quantum circuits by preventing interferences between adjacent logical data qubits.</p>","PeriodicalId":11901,"journal":{"name":"ETRI Journal","volume":"46 5","pages":"759-773"},"PeriodicalIF":1.3,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.4218/etrij.2024-0129","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524668","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":"Asynchronous interface circuit for nonlinear connectivity in multicore spiking neural networks","authors":"Sung-Eun Kim,&nbsp;Kwang-Il Oh,&nbsp;Taewook Kang,&nbsp;Sukho Lee,&nbsp;Hyuk Kim,&nbsp;Mi-Jeong Park,&nbsp;Jae-Jin Lee","doi":"10.4218/etrij.2024-0135","DOIUrl":"https://doi.org/10.4218/etrij.2024-0135","url":null,"abstract":"<p>To expand the scale of spiking neural networks (SNNs), an interface circuit that supports multiple SNN cores is essential. This circuit should be designed using an asynchronous approach to leverage characteristics of SNNs similar to those of the human brain. However, the absence of a global clock presents timing issues during implementation. Hence, we propose an intermediate latching template to establish asynchronous nonlinear connectivity with multipipeline processing between multiple SNN cores. We design arbitration and distribution blocks in the interface circuit based on the proposed template and fabricate an interface circuit that supports four SNN cores using a full-custom approach in a 28-nm CMOS (complementary metal–oxide–semiconductor) FDSOI (fully depleted silicon on insulator) process. The proposed template can enhance throughput in the interface circuit by up to 53% compared with the conventional asynchronous template. The interface circuit transmits spikes while consuming 1.7 and 3.7 pJ of power, supporting 606 and 59 Mevent/s in intrachip and interchip communications, respectively.</p>","PeriodicalId":11901,"journal":{"name":"ETRI Journal","volume":"46 5","pages":"878-889"},"PeriodicalIF":1.3,"publicationDate":"2024-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.4218/etrij.2024-0135","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524645","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":"XEM: Tensor accelerator for AB21 supercomputing artificial intelligence processor","authors":"Won Jeon,&nbsp;Mi Young Lee,&nbsp;Joo Hyun Lee,&nbsp;Chun-Gi Lyuh","doi":"10.4218/etrij.2024-0141","DOIUrl":"https://doi.org/10.4218/etrij.2024-0141","url":null,"abstract":"<p>As computing systems become increasingly larger, high-performance computing (HPC) is gaining importance. In particular, as hyperscale artificial intelligence (AI) applications, such as large language models emerge, HPC has become important even in the field of AI. Important operations in hyperscale AI and HPC are mainly linear algebraic operations based on tensors. An AB21 supercomputing AI processor has been proposed to accelerate such applications. This study proposes a XEM accelerator to accelerate linear algebraic operations in an AB21 processor effectively. The XEM accelerator has outer product-based parallel floating-point units that can efficiently process tensor operations. We provide hardware details of the XEM architecture and introduce new instructions for controlling the XEM accelerator. Additionally, hardware characteristic analyses based on chip fabrication and simulator-based functional verification are conducted. In the future, the performance and functionalities of the XEM accelerator will be verified using an AB21 processor.</p>","PeriodicalId":11901,"journal":{"name":"ETRI Journal","volume":"46 5","pages":"839-850"},"PeriodicalIF":1.3,"publicationDate":"2024-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.4218/etrij.2024-0141","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524642","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":"Quantum electrodynamical formulation of photochemical acid generation and its implications on optical lithography","authors":"Seungjin Lee","doi":"10.4218/etrij.2024-0127","DOIUrl":"https://doi.org/10.4218/etrij.2024-0127","url":null,"abstract":"<p>The photochemical acid generation is refined from the first principles of quantum electrodynamics. First, we briefly review the formulation of the quantum theory of light based on the quantum electrodynamics framework to establish the probability of acid generation at a given spacetime point. The quantum mechanical acid generation is then combined with the deprotection mechanism to obtain a probabilistic description of the deprotection density directly related to feature formation in a photoresist. A statistical analysis of the random deprotection density is presented to reveal the leading characteristics of stochastic feature formation.</p>","PeriodicalId":11901,"journal":{"name":"ETRI Journal","volume":"46 5","pages":"774-782"},"PeriodicalIF":1.3,"publicationDate":"2024-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.4218/etrij.2024-0127","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524643","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}