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High-speed and low-power molecular dynamics processing unit(MDPU)with ab initio accuracy

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npj Computational Materials 2024年第1期10卷 559-568页

作者： Pinghui Mo Yujia Zhang Zhuoying Zhao Hanhan Sun Junhua Li Dawei Guan Xi Ding Xin Zhang Bo Chen Mengchao Shi Duo Zhang Denghui Lu Yinan Wang Jianxing Huang Fei Liu Xinyu Li Mohan Chen Jun Cheng Bin Liang Weinan E Jiayu Dai Linfeng Zhang Han Wang Jie Liu College of Integrated Circuits Hunan UniversityChangsha410082P.R.China College of Electrical and Information Engineering Hunan UniversityChangsha410082P.R.China College of Computer National University of Defense TechnologyChangsha410073P.R.China Department of Physics National University of Defense TechnologyChangsha410073P.R.China DP Technology 100080 BeijingP.R.China AI for Science Institute 100080 BeijingP.R.China Academy for Advanced Interdisciplinary Studies Peking University100871 BeijingP.R.China HEDPS CAPTCollege of EngineeringPeking University100871 BeijingP.R.China School of Mathematical Science Peking University100871 BeijingP.R.China State Key Laboratory of Physical Chemistry of Solid Surfaces iChEMCollege of Chemistry and Chemical EngineeringXiamen UniversityXiamen361005P.R.China School of Integrated Circuits Peking University100871 BeijingP.R.China Center for Machine Learning Research Peking University100871 BeijingP.R.China Institute of Applied Physics and Computational Mathematics 100088 BeijingP.R.China Greater Bay Area Institute for Innovation Hunan UniversityGuangzhou511300P.R.China Department of Electrical and Computer Engineering University of WashingtonSeattleWA98195USA

Molecular dynamics(MD)is an indispensable atomistic-scale computational tool widely-used in various *** the past decades,nearly all ab initio MD and machine-learning MD have been based on the general-purpose central/graphics processing units(CPU/GPU),which are well-known to suffer from their intrinsic“memory wall”and“power wall”***,nowadays MD calculations with ab initio accuracy are extremely time-consuming and power-consuming,imposing serious restrictions on the MD simulation size and *** solve this problem,here we propose a special-purpose MD processing unit(MDPU),which could reduce MD time and power consumption by about 103 times(109 times)compared to state-of-the-art machine-learningMD(ab initio MD)based on CPU/GPU,while keeping ab initio *** significantly-enhanced performance,the proposed MDPU may pave a way for the accurate atomistic-scale analysis of large-size and/or longduration problems which were impossible/impractical to compute before.

关键词： consuming power MDP

VRGrip: Developing a Grip Strength Training Platform Integrating a Wireless E-Textile Forearm Band with an Adaptive 3D VR Game Environment

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VRGrip: Developing a Grip Strength Training Platform Integra...

IEEE MIT Undergraduate Research Technology Conference (URTC)

作者： Lohith Chatragadda aiden Fletcher Matthew J. Delmonico Kunal Mankodiya Dhaval Solanki Interdisciplinary Neuroscience Program University of Rhode Island Kingston RI USA Department of Kinesiology University of Rhode Island Kingston RI USA Department of Electrical Computer and Biomedical Engineering University of Rhode Island Kingston RI USA

ISBN: (数字)9798350309652

ISBN: (纸本)9798350309669

Neurodegenerative disease is a growing global problem. Many of these diseases such as Parkinson's disease can cause grip strength weakness. In this work, we focused on developing an e-textile based EMG acquisition system interfaced with an adaptive VR game engine for detecting hand squeeze actions. We prototyped and performed preliminary tests on a grip detection system consisting of: 1) an e-textile forearm band (E-band), 2) a portable data acquisition module (DAM), and 3) an adaptive 3D VR game engine. Digital health solutions such as our VRGrip system widen the scope of in-home rehabilitation and assessment, and we aimed to make the first step in creating a system for grip strength rehabilitation. To validate our concept, we performed preliminary tests. These tests revealed that the Bluetooth communication protocol between DAM and the game engine was reliable, that the adaptive game engine successfully tailors the game's difficulty level to the user's performance, and that the system was reliable at detecting hand squeezes (F=0.96).

关键词： Wireless communication Training Adaptive systems Three-dimensional displays Protocols Dams Games

EMGrip: Integrating an e-textile forearm band with a computer game to detect changes in grip exertion

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EMGrip: Integrating an e-textile forearm band with a compute...

International Workshop on Wearable and Implantable Body Sensor Networks (BSN)

作者： Lohith Chatragadda Adrian Valdez Franco Kunal Mankodiya Matthew J. Delmonico Dhaval Solanki Interdisciplinary Neuroscience Program University of Rhode Island Kingston RI USA Department of Kinesiology University of Rhode Island Kingston RI USA Department of Electrical Computer and Biomedical Engineering University of Rhode Island Kingston RI USA

An increasing number of people are being diagnosed with neurodegenerative diseases every year. One of the aims of digital health is to provide innovative solutions for rehabilitation or pre-diagnosis of these diseases. In this vein, e-textiles can allow for the construction of flexible and comfortable devices to record biosignals and develop novel rehabilitation platforms. In this study, an e-textile forearm band was constructed to acquire an electromyogram (EMG: a muscle response to a nerve’s stimulation of the muscle) from the flexor muscles of the hand to detect hand grips/squeezes. A light and portable data acquisition module, as well as a computer-based virtual reality game (controlled by the acquired EMG signal) were designed. Our pilot study showed that the system is reliable in detecting hand squeezes in both 6 healthy participants (F-score: 0.88) and 6 participants with Parkinson’s disease (F-score: 0.88). Participants also rated the system to be enjoyable, comfortable, and easy to use.

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Semantic Communications for Artificial Intelligence Generated Content (aiGC) Toward Effective Content Creation

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arXiv 2023年

作者： Liu, Guangyuan Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Kim, Dong In Shen, Xuemin The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Computer Science and Engineering Nanyang Technological University Singapore The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of The Department of Electrical and Computer Engineering University of Waterloo Canada

Artificial Intelligence Generated Content (aiGC) Services have significant potential in digital content creation. The distinctive abilities of aiGC, such as content generation based on minimal input, hold huge potential, especially when integrating with semantic communication (SemCom). In this paper, a novel comprehensive conceptual model for the integration of aiGC and SemCom is developed. Particularly, a content generation level is introduced on top of the semantic level that provides a clear outline of how aiGC and SemCom interact with each other to produce meaningful and effective content. Moreover, a novel framework that employs aiGC technology is proposed as an encoder and decoder for semantic information, considering the joint optimization of semantic extraction and evaluation metrics tailored to aiGC services. The framework can adapt to different types of content generated, the required quality and the semantic information utilized. By employing a Deep Q Network (DQN), a case study is presented that provides useful insights into the feasibility of the optimization problem and its convergence characteristics. © 2023, CC BY.

关键词： Wireless networks

Dataset Condensation with Contrastive Signals

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arXiv 2022年

作者： Lee, Saehyung Chun, Sanghyuk Jung, Sangwon Yun, Sangdoo Yoon, Sungroh Department of Electric and Computer Engineering Seoul National University Korea Republic of NAVER AI Lab. Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of

Recent studies have demonstrated that gradient matching-based dataset synthesis, or dataset condensation (DC), methods can achieve state-of-the-art performance when applied to data-efficient learning tasks. However, in this study, we prove that the existing DC methods can perform worse than the random selection method when task-irrelevant information forms a significant part of the training dataset. We attribute this to the lack of participation of the contrastive signals between the classes resulting from the class-wise gradient matching strategy. To address this problem, we propose Dataset Condensation with Contrastive signals (DCC) by modifying the loss function to enable the DC methods to effectively capture the differences between classes. In addition, we analyze the new loss function in terms of training dynamics by tracking the kernel velocity. Furthermore, we introduce a bi-level warm-up strategy to stabilize the optimization. Our experimental results indicate that while the existing methods are ineffective for fine-grained image classification tasks, the proposed method can successfully generate informative synthetic datasets for the same tasks. Moreover, we demonstrate that the proposed method outperforms the baselines even on benchmark datasets such as SVHN, CIFAR-10, and CIFAR-100. Finally, we demonstrate the high applicability of the proposed method by applying it to continual learning tasks. Copyright © 2022, The Authors. All rights reserved.

关键词： Condensation

Multi-Level Branched Regularization for Federated Learning

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arXiv 2022年

作者： Kim, Jinkyu Kim, Geeho Han, Bohyung Computer Vision Laboratory Department of Electrical and Computer Engineering & ASRI Seoul National University Korea Republic of Interdisciplinary Program of Artificial Intelligence Seoul National University Korea Republic of

A critical challenge of federated learning is data heterogeneity and imbalance across clients, which leads to inconsistency between local networks and unstable convergence of global models. To alleviate the limitations, we propose a novel architectural regularization technique that constructs multiple auxiliary branches in each local model by grafting local and global subnetworks at several different levels and that learns the representations of the main pathway in the local model congruent to the auxiliary hybrid pathways via online knowledge distillation. The proposed technique is effective to robustify the global model even in the non-iid setting and is applicable to various federated learning frameworks conveniently without incurring extra communication costs. We perform comprehensive empirical studies and demonstrate remarkable performance gains in terms of accuracy and efficiency compared to existing methods. The source code is available in our project page1 Copyright © 2022, The Authors. All rights reserved.

关键词： Distillation

On the Analysis of Cross-Lingual Prompt Tuning for Decoder-based Multilingual Model

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arXiv 2023年

作者： Park, Nohil Park, Joonsuk Yoo, Kang Min Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of Artificial Intelligence Institute Seoul National University Korea Republic of NAVER Cloud Korea Republic of NAVER AI Lab Korea Republic of University of Richmond United States

An exciting advancement in the field of multilingual models is the emergence of autoregressive models with zero- and few-shot capabilities, a phenomenon widely reported in large-scale language models. To further improve model adaptation to cross-lingual tasks, another trend is to further fine-tune the language models with either full fine-tuning or parameter-efficient tuning. However, the interaction between parameter-efficient fine-tuning (PEFT) and cross-lingual tasks in multilingual autoregressive models has yet to be studied. Specifically, we lack an understanding of the role of linguistic distributions in multilingual models in the effectiveness of token-based prompt tuning. To address this question, we conduct experiments comparing prompt tuning and fine-tuning on the decoder-based multilingual model, XGLM, with four cross-lingual tasks (XNLI, PAWS-X, POS, NER). According to our study, prompt tuning achieves on par or better performance over fine-tuning across all languages while updating at most 0.13% of the model parameters. Moreover, we empirically show that prompt tuning is more effective in enhancing the performance of low-resource languages than fine-tuning. Our further analysis shows that the phenomenon is related to the tokenization scheme of the multilingual model. © 2023, CC BY.

关键词： Decoding

Ev-TTA: Test-Time Adaptation for Event-Based Object Recognition

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arXiv 2022年

作者： Kim, Junho Hwang, Inwoo Kim, Young Min Department of Electrical and Computer Engineering Seoul National University Korea Republic of Interdisciplinary Program in Artificial Intelligence INMC Seoul National University Korea Republic of

We introduce Ev-TTA, a simple, effective test-time adaptation algorithm for event-based object recognition. While event cameras are proposed to provide measurements of scenes with fast motions or drastic illumination changes, many existing event-based recognition algorithms suffer from performance deterioration under extreme conditions due to significant domain shifts. Ev-TTA mitigates the severe domain gaps by fine-tuning the pre-trained classifiers during the test phase using loss functions inspired by the spatio-temporal characteristics of events. Since the event data is a temporal stream of measurements, our loss function enforces similar predictions for adjacent events to quickly adapt to the changed environment online. Also, we utilize the spatial correlations between two polarities of events to handle noise under extreme illumination, where different polarities of events exhibit distinctive noise distributions. Ev-TTA demonstrates a large amount of performance gain on a wide range of event-based object recognition tasks without extensive additional training. Our formulation can be successfully applied regardless of input representations and further extended into regression tasks. We expect Ev-TTA to provide the key technique to deploy event-based vision algorithms in challenging real-world applications where significant domain shift is inevitable. © 2022, CC BY.

关键词： Object recognition

Photonic Crystals with Split Ring Unit Cells for Subwavelength Light Confinement

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Photonic Crystals with Split Ring Unit Cells for Subwaveleng...

Conference on Lasers and Electro-Optics (CLEO)

作者： Kellen P. Arnold Sami I. Halimi Joshua A. Allen Shuren Hu Sharon M. Weiss Interdisciplinary Materials Science Program Vanderbilt University Nashville TN USA Department of Electrical Engineering and Computer Science Vanderbilt University Nashville TN USA

We report a split ring photonic crystal that demonstrates an order of magnitude larger peak energy density compared to traditional photonic crystals. The split ring offers highly focused optical energy in an accessibl... 详细信息

ISBN: (数字)9781957171050

ISBN: (纸本)9781665466660

关键词： Optical resonators Photonic crystals Ring lasers Electrooptical waveguides Photonics