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arXiv 2021年

作者： Xu, Shaofu Wang, Jing Shu, Haowen Zhang, Zhike Yi, Sicheng Bai, Bowen Wang, Xingjun Liu, Jianguo Zou, Weiwen Department of Electronic Engineering Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China State Key Laboratory of Advanced Optical Communications System and Networks Department of Electronics School of Electronics Engineering and Computer Science Peking University Beijing100871 China Institution of Semiconductors Chinese Academy of Sciences Beijing100083 China

Optical implementations of neural networks (ONNs) herald the next-generation high-speed and energy-efficient deep learning computing by harnessing the technical advantages of large bandwidth and high parallelism of optics. However, due to the problems of incomplete numerical domain, limited hardware scale, or inadequate numerical accuracy, the majority of existing ONNs were studied for basic classification tasks. Given that regression is a fundamental form of deep learning and accounts for a large part of current artificial intelligence applications, it is necessary to master deep learning regression for further development and deployment of ONNs. Here, we demonstrate a silicon-based optical coherent dot-product chip (OCDC) capable of completing deep learning regression tasks. The OCDC adopts optical fields to carry out operations in complete real-value domain instead of in only positive domain. Via reusing, a single chip conducts matrix multiplications and convolutions in neural networks of any complexity. Also, hardware deviations are compensated via in-situ backpropagation control provided the simplicity of chip architecture. Therefore, the OCDC meets the requirements for sophisticated regression tasks and we successfully demonstrate a representative neural network, the AUTOMAP (a cutting-edge neural network model for image reconstruction). The quality of reconstructed images by the OCDC and a 32-bit digital computer is comparable. To the best of our knowledge, there is no precedent of performing such state-of-the-art regression tasks on ONN chip. It is anticipated that the OCDC can promote novel accomplishment of ONNs in modern AI applications including autonomous driving, natural language processing, and scientific study. © 2021, CC BY-NC-SA.

关键词： Deep learning

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PAM-4 Optical Transmission Beyond 224 Gbps Based on an Ultrahigh-Bandwidth Slow-light Silicon Modulator

PAM-4 Optical Transmission Beyond 224 Gbps Based on an Ultra...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Changhao Han Jun Qin Qipeng Yang Zhao Zheng Haowen Shu Yunhao Zhang Yichen Wu Yu Sun Junde Lu Yan Zhou Zhangfeng Ge Lei Wang Zhixue He Shaohua Yu Weiwei Hu Chao Peng John E. Bowers Xingjun Wang State Key Laboratory of Advanced Optical Communications System and Networks School of Electronics Peking University Beijing China Department of Electrical and Computer Engineering University of California Santa Barbara CA US Key Laboratory of Information and Communication Systems Ministry of Information Industry Beijing Information Science and Technology University Beijing China Frontiers Science Center for Nano-optoelectronics Peking University Beijing China Peng Cheng Laboratory Shenzhen China Peking University Yangtze Delta Institute of Optoelectronics Nantong China

We experimentally demonstrate PAM-4 optical transmission beyond 224 Gbps based on an ultrahigh-bandwidth slow-light silicon modulator in C-band with the combination of the artificial neural network equalizers.

ISBN: (纸本)9798350369311

关键词： Equalizers C-band Optical computing Artificial neural networks Electro-optic modulators Optical fiber networks Silicon photonics Ultrafast optics Optical modulation Passband

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Parallel chaotic sources based on microcombs

Parallel chaotic sources based on microcombs

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Conference on Lasers and Electro-Optics (CLEO)

作者： Bitao Shen Haowen Shu Weiqiang Xie Ruixuan Chen Zhi Liu Xuguang Zhang Yimeng Wang Yunhao Zhang Buwen Cheng Shaohua Yu John E. Bowers Lin Chang Xingjun Wang State Key Laboratory of Advanced Optical Communications System and Networks School of Electronics Peking University Beijing China Department of Electrical and Computer Engineering University of California Santa Barbara CA USA State Key Laboratory on Integrated Optoelectronics Institute of Semiconductors Chinese Academy of Sciences Beijing China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing China Peng Cheng Laboratory Shenzhen China Frontiers Science Center for Nano-Optoelectronics Peking University Beijing China

Massively parallel optical chaotic sources with large bandwidth are demonstrated based on chaotic microcombs in the AlGaAsOI platform. 30-channel parallel random bit generation with 30 Gbit/s per channel is realized b... 详细信息

ISBN: (纸本)9781957171258

关键词： Diode lasers Integrated photonics Kerr effect Nonlinear effects Optical microscopy Random number generation

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High-coherence parallelization in integrated photonics

arXiv

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arXiv 2023年

作者： Zhang, Xuguang Zhou, Zixuan Guo, Yijun Zhuang, Minxue Jin, Warren Shen, Bitao Chen, Yujun Huang, Jiahui Tao, Zihan Jin, Ming Chen, Ruixuan Ge, Zhangfeng Fang, Zhou Zhang, Ning Liu, Yadong Cai, Pengfei Hu, Weiwei Shu, Haowen Pan, Dong Bowers, John E. Wang, Xingjun Chang, Lin State Key Laboratory of Advanced Optical Communications System and Networks School of Electronics Peking University Beijing China Department of Electrical and Computer Engineering University of California Santa Barbara Santa BarbaraCA United States Frontiers Science Center for Nano-optoelectronics Peking University Beijing China SiFotonics Technologies Co. Ltd. Beijing China Peking University Yangtze Delta Institute of Optoelectronics Nantong China

Coherent optics has profoundly impacted diverse applications ranging from communications, LiDAR to quantum computations. However, building coherent systems in integrated photonics previously came at great expense in hardware integration and energy efficiency: the lack of a power-efficient way to generate highly coherent light necessitates bulky lasers and amplifiers, while frequency and phase recovery schemes require huge digital signal processing resources. In this work, we demonstrate a high-coherence parallelization strategy that facilitates advanced integrated coherent systems at a minimum price. Using a self-injection locked microcomb to injection lock a distributed feedback laser array, we boost the microcomb power by a record high gain of up to 60 dB on chip with no degradation in coherence. This strategy enables tens of highly coherent channels with an intrinsic linewidth down to the 10 Hz level and power of more than 20 dBm. The overall electrical to optical wall-plug efficiency reaches 19%, comparable with that of the state-of-the-art semiconductor lasers. Driven by this parallel source, we demonstrate a silicon photonic communication link with an unprecedented data rate beyond 60 Tbit/s. Importantly, the high coherence we achieve reduces the coherent-related DSP consumption by 99.999% compared with the traditional III-V laser pump scheme. This work paves a way to realizing scalable, high-performance coherent integrated photonic systems, potentially benefiting numerous applications. Copyright © 2023, The Authors. All rights reserved.

关键词： Energy efficiency

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Asset Class Market Investment Portfolio Analysis and Tracking

Asset Class Market Investment Portfolio Analysis and Trackin...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Pramod A Jadhav C. Vinotha Sushil Kumar Gupta Bijesh Dhyani Vinod H Patil Rohit Kumar Department of Computer Science & Businress System Bharati Vidyapeeth (Deemed to be University) College of Engineering Pune Maharashtra India Department of MBA Sri Krishna College of Engineering & Technology Coimbatore Tamilnadu India School of Management (UG) Dr. Vishwanath Karad MIT World Peace University Pune Maharashtra India Depaertment of Management Studies Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Electronics & Telecommunication Bharati Vidyapeeth (Deemed to be University) College of Engineering Puner Maharashtra India Department of Humanities Lloyd Institute of Engineering and Technology Greater Noida Uttar Pradesh India

Researchers and analysts have, for a long time, taken an interest in the expectations that consumers have regarding stock marketing. There is a widespread perception that trying to forecast what will happen in the stock market is fruitless since the market behaves fundamentally like a random walk. Predicting swings in stock prices may be difficult because of the many different elements that are in play. In the short run, the showcase acts as a voting machine, but in the long run, it acts as a scale, which enables the prediction of its long-term trajectory. It seems that the application of machine learning and other technologies to the issues of inventory analysis and cost estimates is a new sector that has a lot of potential. To begin, we will present a concise introduction to stock markets and an in-depth explanation of how to make predictions regarding stock prices. The next step is for us to hone in on many of the most significant problems surrounding the precision of stock research and estimates. The fundamental, intermediate, short-term, and long-term perspectives on stocks, as well as their technical aspects, are explored. In conclusion, we highlight a few problems and investigate possible advancements that the future may hold in this sector.

关键词： Machine learning algorithms Statistical analysis Machine learning Prediction algorithms Internet Trajectory Stock markets

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Improved Conservation of Energy in Fog IOT Services Using Machine Learning Model

Improved Conservation of Energy in Fog IOT Services Using Ma...

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2020 International Conference on Computing and Information Technology, ICCIT 2020

作者： Prakash, M. Viju Porkodi, V. Rajanarayanan, S. Khan, Mujeebudheen Ibrahim, Banar Fareed Sivaram, M. College of Science Knowledge University Department of Computer Science Erbil Kurdistan Region Iraq Lebanese French University Department of Information Technology Erbil Kurdistan Region Iraq Arba Minch University Department of Business Administration and Information System Ethiopia Ibra College of Technology Department of Electrical and Electronics Oman Cyprus International University Computer Engineering Department Cyprus Lebanese French University Department of Computer Networking Erbil Kurdistan Region Iraq

ISBN: (纸本)9781728126807

This Fog Computing is an extension of Cloud Computing technology to the network edge, which enables a newer breed of services and applications. Resource sharing and resource discovery is considered to be critical for the performance of fog computing applications. In general, the methods to reduce are consumption of energy in heterogonous network is very minimal. Considering the higher consumption of energy in heterogeneous networks as a problem statement, this work proposes a machine learning model to reduce the consumption of energy demand in fog computing Internet of Things (IoT) services. Considering the problem, the machine learning model adopts network density, latency and mobility as its energy constraints and designs an objective function to support the lower energy consumption in the network. The simulation of the proposed method is carried out between the proposed and existing methods in terms of various performance metrics. The result shows that the proposed machine learning method in Fog IoT environment is efficient in conserving the energy than the other methods. © 2020 IEEE.

关键词： Fog computing

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Bridging microcombs and silicon photonic engines for optoelectronics systems

arXiv

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arXiv 2021年

作者： Shu, Haowen Chang, Lin Tao, Yuansheng Shen, Bitao Xie, Weiqiang Jin, Ming Netherton, Andrew Tao, Zihan Zhang, Xuguang Chen, Ruixuan Bai, Bowen Qin, Jun Yu, Shaohua Wang, Xingjun Bowers, John E. State Key Laboratory of Advanced Optical Communications System and Networks Department of Electronics School of Electronics Engineering and Computer Science Peking University Beijing100871 China Department of Electrical and Computer Engineering University of California Santa BarbaraCA93106 United States Peng Cheng Laboratory Shenzhen518055 China Frontiers Science Center for Nano-optoelectronics Peking University Beijing100871 China

Microcombs have sparked a surge of applications over the last decade, ranging from optical communications to metrology. Despite their diverse deployment, most microcomb-based systems rely on a tremendous amount of bulk equipment to fulfill their desired functions, which is rather complicated, expensive and power-consuming. On the other hand, foundry-based silicon photonics (SiPh) has had remarkable success in providing versatile functionality in a scalable and low-cost manner, but its available chip-based light sources lack the capacity for parallelization, which limits the scope of SiPh applications. Here, we bridge these two technologies by using a power-efficient and operationally-simple AlGaAs on insulator microcomb source to drive CMOS SiPh engines. We present two important chip-scale photonic systems for optical data transmissions and microwave photonics respectively: The first microcomb-based integrated photonic data link is demonstrated, based on a pulse-amplitude 4-level modulation scheme with 2 Tbps aggregate rate, and a highly reconfigurable microwave photonic filter with unprecedented integration level is constructed, using a time stretch scheme. Such synergy of microcomb and SiPh integrated components is an essential step towards the next generation of fully integrated photonic systems. Copyright © 2021, The Authors. All rights reserved.

关键词： Light transmission

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Reconstructing Suspended Sediment Concentrations in the Mekong River Basin Via Semi-Supervised-Based Deep Neural Networks

SSRN

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SSRN 2024年

作者： Nguyen, Thi-Thu-Ha Vu, Duc Quang Doan, Ngoc Phu Thi Khanh Chi, Huynh Li, Peixin Van Binh, Doan An, Yimeng Tuan Dung, Pham Hoang, Tuan A. Thai Son, Mai Laboratory of Environmental Sciences and Climate Change Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City70000 Viet Nam Faculty of Environment School of Technology Van Lang University Ho Chi Minh City70000 Viet Nam Department of Computer Science and Information System Thai Nguyen University of Education Viet Nam School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast United Kingdom College of Economics and Management Nanjing University of Aeronautics and Astronautics China Master program in Water Technology Reuse and Management Faculty of Engineering Vietnamese-German University Binh Duong Province Ben Cat Town820000 Viet Nam

The Mekong River Basin (MRB) is one of the highest biodiversity areas in the world and is the vital source of livelihoods for more than 60 millions of people over six countries in Southeast Asia. However, it is suffering from a severe sediment starvation problem caused by climate change and human activities, particularly dam constructions. Understanding long-term sediment changes thus plays a crucial role for management and contingency plans. Current sediment concentration data in the MRB are extremely sporadic, making it a non-trivial task to analyze them. This paper focuses on the sediment reconstruction to provide long-term reliable suspended sediment concentration (SSC) data using Machine Learning (ML) perspectives. In particular, a novel semi-supervised ML model, which can effectively deal with data sparsity, is introduced to predict/reconstruct missing sediment data. The key idea of this approach is to exploit abundant available hydroclimate data to reduce training overfitting rather than solely relying on sediment concentration data, thus enhancing the accuracy of the employed ML models. Extensive experiments on data obtained from 1979 to 2019 at the three main stations (i.e., Chiang Saen, Nong Khai, and Mukdahan) are conducted to demonstrate the performances of the proposed method compared to other state-of-the-art supervised techniques and traditional simulation methods commonly used in previous sediment forecasting/reconstruction studies. To the best of our knowledge, this approach is the first semi-supervised method to reconstruct sediment data in the field. Though this study focuses on the MRB, sediment sparsity is a very common problem in other river systems worldwide. Thus, this method can be widely applied for them for more effective sediment reconstruction/prediction in general. © 2024, The Authors. All rights reserved.

关键词： Biodiversity

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Disjoint-Path and Golden-Pin Based Irregular PCB Routing with Complex Constraints

Disjoint-Path and Golden-Pin Based Irregular PCB Routing wit...

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Design Automation Conference

作者： Qinghai Liu Qinfei Tang Jiarui Chen Chuandong Chen Ziran Zhu Huan He Jianli Chen Yao-Wen Chang Center for Discrete Mathematics Fuzhou University Fuzhou China Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China Fuzhou China College of Computer and Date Science Fuzhou University Fuzhou China Department of Microelectronics Fuzhou University Fuzhou China Department of Microelectronics Southeast University Nanjing China Hangzhou Huawei Enterprises Telecommunication Technologies Co. Ltd Hangzhou China State Key Lab of ASIC & System Fudan University Shanghai China Graduate Institute of Electronics Engineering National Taiwan University Taipei Taiwan Department of Electrical Engineering National Taiwan University Taipei Taiwan

PCB routing becomes time-consuming as the complexity of PCB design increases. Unlike traditional schemes that treat the two essential PCB routing processes separately, namely, escape and bus routing, we consider the continuity between them and present a golden-pin-based routing scheme to find the desired solution with angle and topology constraints. Further, conventional rip-up and reroute methods are often ineffective and inefficient for congestion alleviation and routability optimization. We construct a component graph by modeling components as vertices and applying the minimum weight vertex covering method to improve the routability. A self-adaptable ordering method is presented for escape routing to arrange the pin order on the component boundary, guaranteeing successful bus routing. In addition, escape routing is performed based on a disjoint path method. We construct a dynamic Hanan grid in bus routing and utilize a novel congestion adjustment technique to improve solution quality. Compared with FreeRouting and Allegro, the experiment results show that our algorithm achieves high routability and a significant 90% runtime reduction.

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Mixed-Cell-Height Legalization Considering Technology and Region Constraints

Mixed-Cell-Height Legalization Considering Technology and Re...

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作者： Zhu, Ziran Chen, Jianli Zhu, Wenxing Chang, Yao-Wen Center for Discrete Mathematics and Theoretical Computer Science Fuzhou University Fuzhou China State Key Laboratory of ASIC and System Fudan University Shanghai China Department of Electrical Engineering Graduate Institute of Electronics Engineering National Taiwan University Taipei Taiwan

Mixed-cell-height circuits have become popular in advanced technologies for better power, area, routability, and performance tradeoffs. With technology and region constraints imposed by modern circuit designs, the mixed-cell-height legalization problem has become even more challenging. Additionally, an ideal legalization method should minimize both the average and maximum cell movements to preserve the quality of a given placement as much as possible. In this article, we present an effective and efficient mixed-cell-height legalization algorithm to consider technology and region constraints while minimizing the average and maximum cell movements. We first present a fence region handling technique to unify the fence regions and the default region. To obtain a desired cell assignment, we then propose a movement-aware cell reassignment method by iteratively reassigning cells in locally dense areas to their desired rows. After cell reassignment, a technology-aware legalization is presented to remove cell overlaps while satisfying the technology constraints. Finally, we propose a technology-aware refinement to further reduce the average and maximum cell movements without increasing the technology constraints violations. Compared with the champion of the 2017 CAD Contest at ICCAD and the state-of-the-art work, experimental results based on the 2017 CAD Contest at ICCAD benchmarks show that our algorithm achieves the best average and maximum cell movements and significantly fewer technology constraint violations, in a comparable runtime. The experimental results based on the modified 2015 ISPD Contest benchmarks also demonstrate the effectiveness of our algorithm in minimizing the average and maximum cell movements, compared with state-of-the-art mixed-cell-height legalizers. © 1982-2012 IEEE.

关键词： Iterative methods

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