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SPIRAL+: Efficient Signal-Power Integrity Co-Analysis for Inter-Chiplet Links Validation

IEEE Transactions on computer-Aided Design of Integrated Circuits and syst.ms 2025年

作者： Dong, Xiao Sun, Songyu Jiang, Yangfan Hu, Jingtong Gao, Dawei Shi, Zhiguo Zhuo, Cheng Zhejiang University College of Information Science and Electronic Engineering Hangzhou China University of Pittsburgh Department of Electrical and Computer Engineering PittsburghPA United States Zhejiang University and Zhejiang ICsprout Semiconductor Co. Ltd College of Integrated Circuits Hangzhou China Zhejiang Univ. and Key Lab. of Collab. Sensing and Autonomous Unmanned Syst. of Zhejiang Province College of Integrated Circuits Hangzhou China

Chiplet technology has recently emerged as a promising solution to improving chip performance through the modularization of complex designs and communication facilitated by high-speed inter-chiplet serial links. However, the increasing on-package routing density and data rates of these links introduce complex signal and power integrity challenges, surpassing those encountered in traditional large monolithic chips. Addressing these complexities with efficient analysis and design tools is crucial for maintaining design robustness. In this article, we propose SPIRAL+: a signal-power integrity co-analysis framework for high-speed inter-chiplet serial links validation. The framework employs machine learning to construct transmitter models and utilizes an impulse response extraction method for modeling the channel and receiver. It then performs signal-power integrity co-analysis through a novel double-edge response based method, leveraging the developed equivalent models. Additionally, an efficient machine learning model is crafted to accurately predict eye diagram metrics. The analysis provides valuable insights for design optimization. Experimental results show that SPIRAL+ achieves eye diagrams with a mean relative error of 0.07-7.47%, while realizing a speedup of 31-326× over traditional commercial tools. © 1982-2012 IEEE.

关键词： Impulse response

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Robust Generalization against Photon-Limited Corruptions via Worst-Case Sharpness Minimization

arXiv

引用

arXiv 2023年

作者： Huang, Zhuo Zhu, Miaoxi Xia, Xiaobo Shen, Li Yu, Jun Gong, Chen Han, Bo Du, Bo Liu, Tongliang Syndey Ai Centre The University of Sydney Australia National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China Jd Explore Academy China Department of Automation University of Science and Technology of China China Key Lab. of Intelligent Percept. and Syst. for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology China Department of Computer Science Hong Kong Baptist University Hong Kong

Robust generalization aims to tackle the most challenging data distributions which are rare in the training set and contain severe noises, i.e., photon-limited corruptions. Common solutions such as distributionally robust optimization (DRO) focus on the worst-case empirical risk to ensure low training error on the uncommon noisy distributions. However, due to the over-parameterized model being optimized on scarce worst-case data, DRO fails to produce a smooth loss landscape, thus struggling on generalizing well to the test set. Therefore, instead of focusing on the worst-case risk minimization, we propose SharpDRO by penalizing the sharpness of the worst-case distribution, which measures the loss changes around the neighbor of learning parameters. Through worst-case sharpness minimization, the proposed method successfully produces a flat loss curve on the corrupted distributions, thus achieving robust generalization. Moreover, by considering whether the distribution annotation is available, we apply SharpDRO to two problem settings and design a worst-case selection process for robust generalization. Theoretically, we show that SharpDRO has a great convergence guarantee. Experimentally, we simulate photon-limited corruptions using CIFAR10/100 and ImageNet30 datasets and show that SharpDRO exhibits a strong generalization ability against severe corruptions and exceeds well-known baseline methods with large performance gains. Copyright © 2023, The Authors. All rights reserved.

关键词： Optimization

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A Three-Fingered Adaptive Gripper with Multiple Grasping Modes

A Three-Fingered Adaptive Gripper with Multiple Grasping Mod...

引用

2021 IEEE/RSJ International Conference on Intelligent Robots and syst.ms, IROS 2021

作者： Kang, Long Yang, Yang Yang, Jian Yi, Byung-Ju Nanjing University of Science and Technology PCA Lab Key Lab. of Intelligent Percept. and Syst. for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing210094 China School of Automation Nanjing University of Information Science and Technology Nanjing210044 China Department of the Electronic Systems Engineering Hanyang University Ansan426-791 Korea Republic of

ISBN: (纸本)9781665417143

This paper presents an underactuated robotic gripper that consists of three fingers. This gripper is driven by seven actuators and capable of grasping a wide range of objects in different working scenarios. A combination of a four-bar mechanism and parallelograms ensures that each finger can provide the basic pinch grasp and power grasp. Detailed fingertip grasping force analysis shows the large payload of this gripper. To fulfill multiple challenging grasping tasks, the fingertip orientation of each finger was designed to be decoupled from the finger flexion motion. Particular emphasis is placed on the environmental contact-based grasp and active transition from the pinch grasp to the power grasp. Detailed analysis shows that the contact-based fingertip grasp could be used to grasp thin objects lying on a flat surface with widths less than 168 mm safely and stably. To prevent over-squeezing of the grasped object in the active transition, the fingertip orientation and finger flexion motion were controlled coordinately. Moreover, a combination of the contact-based grasp and the direct power grasp was also allowed to grasp objects lying on a flat surface and having smooth surfaces robustly. Experimental results demonstrate the effectiveness of the proposed gripper in real-world applications. © 2021 IEEE.

关键词： Grippers

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Dual Interactive Graph Convolutional Networks for Hyperspectral Image Classification

引用

IEEE Transactions on Geoscience and Remote Sensing 2022年 60卷 1页

作者： Wan, Sheng Pan, Shirui Zhong, Ping Chang, Xiaojun Yang, Jian Gong, Chen Pca Laboratory Key Lab. of Intelligent Percept. and Syst. for High-Dimensional Information of Ministry of Education Jiangsu Key Laboratory of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Faculty of Information Technology Monash University ClaytonVIC3800 Australia National Key Laboratory of Science and Technology on Atr National University of Defense Technology Changsha410073 China Pca Laboratory Key Lab. of Intelligent Percept. and Syst. for High-Dimensional Information of Ministry of Education Nanjing University of Science and Technology Nanjing210094 China Department of Computing Hong Kong Polytechnic University Hong Kong Hong Kong

Recently, graph convolutional network (GCN) has progressed significantly and gained increasing attention in hyperspectral image (HSI) classification due to its impressive representation power. However, existing GCN-based methods do not give full consideration to the multiscale spatial information, since the convolution operations are governed by fixed neighborhood. As a result, their performances can be limited, particularly in the regions with diverse land cover appearances. In this article, we develop a new dual interactive GCN (DIGCN) which introduces the dual GCN branches to capture spatial information at different scales. More significantly, the dual interactive module is embedded across the GCN branches, so that the correlation of multiscale spatial information can be leveraged to refine the graph information. To be concrete, the edge information contained in one GCN branch can be refined by incorporating the feature representations from the other branch. Analogously, improved feature representations can be generated in one GCN branch by fusing the edge information from the other branch. As such, the refined graph information can help enhance the representation power of the model. Furthermore, to avoid the negative effects of the manually constructed graph, our proposed model adaptively learns a discriminative region-induced graph, which also accelerates the convolution operation. We comprehensively evaluate the proposed method on four commonly used HSI benchmark data sets, and the state-of-the-art results can be achieved when compared with several typical HSI classification methods. © 1980-2012 IEEE.

关键词： Convolution

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KRADA: Known-region-aware Domain Alignment for Openset Domain Adaptation in Semantic Segmentation

arXiv

引用

arXiv 2021年

作者： Zhou, Chenhong Liu, Feng Gong, Chen Zeng, Rongfei Liu, Tongliang Cheung, William K. Han, Bo Department of Computer Science Hong Kong Baptist University Hong Kong The University of Melbourne Australia The Key Lab. of Intelligent Percept. and Syst. for High-Dimensional Info. of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology China Northeastern University United States Sydney Ai Centre The University of Sydney Australia

In semantic segmentation, we aim to train a pixel-level classifier to assign category labels to all pixels in an image, where labeled training images and unlabeled test images are from the same distribution and share the same label set. However, in an open world, the unlabeled test images probably contain unknown categories and have different distributions from the labeled images. Hence, in this paper, we consider a new, more realistic, and more challenging problem setting where the pixel-level classifier has to be trained with labeled images and unlabeled open-world images-we name it open-set domain adaptation segmentation (OSDAS). In OSDAS, the trained classifier is expected to identify unknown-class pixels and classify known-class pixels well. To solve OSDAS, we first investigate which distribution that unknown-class pixels obey. Then, motivated by the goodness-of-fit test, we use statistical measurements to show how a pixel fits the distribution of an unknown class and select highly-fitted pixels to form the unknown region in each test image. Eventually, we propose an end-to-end learning framework, known-region-aware domain alignment (KRADA), to distinguish unknown classes while aligning the distributions of known classes in labeled and unlabeled open-world images. The effectiveness of KRADA has been verified on two synthetic tasks and one COVID-19 segmentation task. Copyright © 2021, The Authors. All rights reserved.

关键词： Pixels

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Spatial group-wise enhance: Improving semantic feature learning in convolutional networks

arXiv

引用

arXiv 2019年

作者： Li, Xiang Hu, Xiaolin Yang, Jian Pca Lab Key Lab of Intelligent Percept. and Syst. for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Momenta Department of Computer Science and Technology Tsinghua University

The Convolutional Neural Networks (CNNs) generate the feature representation of complex objects by collecting hierarchical and different parts of semantic subfeatures. These sub-features can usually be distributed in grouped form in the feature vector of each layer [43, 32], representing various semantic entities. However, the activation of these sub-features is often spatially affected by similar patterns and noisy backgrounds, resulting in erroneous localization and identification. We propose a Spatial Group-wise Enhance (SGE) module that can adjust the importance of each sub-feature by generating an attention factor for each spatial location in each semantic group, so that every individual group can autonomously enhance its learnt expression and suppress possible noise. The attention factors are only guided by the similarities between the global and local feature descriptors inside each group, thus the design of SGE module is extremely lightweight with almost no extra parameters and calculations. Despite being trained with only category supervisions, the SGE component is extremely effective in highlighting multiple active areas with various high-order semantics (such as the dog's eyes, nose, etc.). When integrated with popular CNN backbones, SGE can significantly boost the performance of image recognition tasks. Specifically, based on ResNet50 backbones, SGE achieves 1.2% Top-1 accuracy improvement on the ImageNet benchmark and 1.0∼2.0% AP gain on the COCO benchmark across a wide range of detectors (Faster/Mask/Cascade RCNN and RetinaNet). Codes and pretrained models are available at https://***/implus/PytorchInsight. Copyright © 2019, The Authors. All rights reserved.

关键词： Semantics

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Efficient Approximation Algorithms for Adaptive Seed Minimization

arXiv

引用

arXiv 2019年

作者： Tang, Jing Huang, Keke Xiao, Xiaokui Lakshmanan, Laks V.S. Tang, Xueyan Sun, Aixin Lim, Andrew Dept. of Ind. Syst. Engg. and Mgmt. National University of Singapore School of Comp. Sci. and Engg Nanyang Technological University School of Computing National University of Singapore Department of Computer Science University of British Columbia School of Computer Science and Engineering Nanyang Technological University

As a dual problem of influence maximization, the seed minimization problem asks for the minimum number of seed nodes to influence a required number η of users in a given social network G. Existing algorithms for seed minimization mostly consider the non-adaptive setting, where all seed nodes are selected in one batch without observing how they may influence other users. In this paper, we study seed minimization in the adaptive setting, where the seed nodes are selected in several batches, such that the choice of a batch may exploit information about the actual influence of the previous batches. We propose a novel algorithm, ASTI, which addresses the adaptive seed minimization problem in O (Formula presented) expected time and offers an approximation guarantee of (1−(1−1/b)b )(1−1/e)(1−Ε ) (ln η+1)2 in expectation, where η is the targeted number of influenced nodes, b is size of each seed node batch, and Ε ∈ (0, 1) is a user-specified parameter. To the best of our knowledge, ASTI is the first algorithm that provides such an approximation guarantee without incurring prohibitive computation overhead. With extensive experiments on a variety of datasets, we demonstrate the effectiveness and efficiency of ASTI over competing methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Approximation algorithms

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Terahertz dynamic aperture imaging at stand-off distances using a compressed sensing protocol

arXiv

引用

arXiv 2019年

作者： Augustin, S. Jung, P. Frohmann, S. Hübers, H.-W. Dept. of Phys. Humboldt-Univ. zu Berlin and the Inst. of Optical Sensor Syst. of the Ger. Aerosp. Ctr. Berlin Department of Electrical Engineering and Computer Science Technische Universität Berlin Institute of Optical Sensor Systems of the German Aerospace Center Berlin

In this text, results of a 0:35 terahertz (THz) dynamic aperture imaging approach are presented. The experiments use an optical modulation approach and a single pixel detector at a stand-off imaging distance of ≈1m. The optical modulation creates dynamic apertures of 5cm diameter with ≈2000 individually controllable elements. An optical modulation approach is used here for the first time at a large far-field distance, for the investigation of various test targets in a fieldof-view of 8 x 8cm. The results highlight the versatility of this modulation technique and show that this imaging paradigm is applicable even at large far-field distances. It proves the feasibility of this imaging approach for potential applications like stand-off security imaging or far field THz microscopy. Copyright © 2019, The Authors. All rights reserved.

关键词： Light modulators

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Zero-resource neural machine translation with multi-agent communication game

arXiv

引用

arXiv 2018年

作者： Chen, Yun Liu, Yang Li, Victor O.K. Department of Electrical and Electronic Engineering University of Hong Kong State Key Lab. of Intelligent Technol. and Syst. Tsinghua Natl. Lab. for Info. Sci. and Technology Department of Computer Science and Technology Tsinghua University Beijing China

While end-to-end neural machine translation (NMT) has achieved notable success in the past years in translating a handful of resource-rich language pairs, it still suffers from the data scarcity problem for low-resource language pairs and domains. To tackle this problem, we propose an interactive multimodal framework for zero-resource neural machine translation. Instead of being passively exposed to large amounts of parallel corpora, our learners (implemented as encoder-decoder architecture) engage in cooperative image description games, and thus develop their own image captioning or neural machine translation model from the need to communicate in order to succeed at the game. Experimental results on the IAPR-TC12 and Multi30K datasets show that the proposed learning mechanism significantly improves over the state-of-the-art methods. Copyright © 2018, The Authors. All rights reserved.

关键词： Neural machine translation

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Solving most syst.ms of random quadratic equations 17

Solving most systems of random quadratic equations

引用

Proceedings of the 31st International Conference on Neural Information Processing syst.ms

作者： Gang Wang Georgios B. Giannakis Yousef Saad Jie Chen Key Lab of Intell. Contr. and Decision of Complex Syst. Beijing Inst. of Technology and Digital Tech. Center & Dept. of Electrical and Computer Eng. Univ. of Minnesota Digital Tech. Center & Dept. of Electrical and Computer Eng. Univ. of Minnesota Department of Computer Science and Engineering Univ. of Minnesota Key Lab of Intell. Contr. and Decision of Complex Syst. Beijing Inst. of Technology

ISBN: (纸本)9781510860964

This paper deals with finding an n-dimensional solution x to a syst.m of quadratic equations yi = ||2, 1 ≤ i ≤ m, which in general is known to be NP-hard. We put forth a novel procedure, that starts with a weighted maximal correlation initialization obtainable with a few power iterations, followed by successive refinements based on iteratively reweighted gradient-type iterations. The novel techniques distinguish themselves from prior works by the inclusion of a fresh (re)weighting regularization. For certain random measurement models, the proposed procedure returns the true solution x with high probability in time proportional to reading the data {(ai; yi)]1≤i≤m, provided that the number m of equations is some constant c > 0 times the number n of unknowns, that is, m ≤ cn. Empirically, the upshots of this contribution are: i) perfect signal recovery in the high-dimensional regime given only an information-theoretic limit number of equations; and, ii) (near-)optimal statistical accuracy in the presence of additive noise. Extensive numerical tests using both synthetic data and real images corroborate its improved signal recovery performance and computational efficiency relative to state-of-the-art approaches.

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