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IEEE Computer Society Annual Symposium on VLSI

作者： Debasmita Bhoumik Ritajit Majumdar Dhiraj Madan Susmita Sur-Kolay Advanced Computing and Microelectronics Unit Indian Statistical Institute India IBM Quantum IBM India Research Lab

ISBN: (数字)9798350354119

ISBN: (纸本)9798350354126

Decoding error syndromes for topological quantum error correcting codes, such as surface and heavy hexagonal codes, is computationally expensive. While minimum weight perfect matching (MWPM) algorithms have been commonly used for decoding, recent works have demonstrated the efficacy of machine learning (ML), particularly neural networks, in decoding syndromes for these codes. In this study, we introduce a ML-based decoder tailored to heavy hexagonal code to address asymmetric noise channels which reflect real-world scenario better than the depolarization model considered in previous works. Our proposed decoder shows $\sim 5\times$ and $\sim 22\times$ improvements in the threshold values for amplitude and amplitude-phase damping noise models respectively over MWPM methods. Our decoder is also robust to changes in asymmetry, with the threshold reducing by only ~ 3.6% for a $10\times$ change in asymmetry.

关键词： Damping Quantum computing Machine learning algorithms Noise Neural networks Machine learning Very large scale integration

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Breaking the Million-Electron and 1 EFLOP/s Barriers: Biomolecular-Scale Ab Initio Molecular Dynamics Using MP2 Potentials

arXiv

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arXiv 2024年

作者： Stocks, Ryan Galvez Vallejo, Jorge L. Yu, Fiona C.Y. Snowdon, Calum Palethorpe, Elise Kurzak, Jakub Bykov, Dmytro Barca, Giuseppe M.J. School of Computing Australian National University Australia Advanced Micro Devices Inc. Australia Computing for Chemistry Oak Ridge National Lab United States School of Computing University of Melbourne Australia

The accurate simulation of complex biochemical phenomena has historically been hampered by the computational requirements of high-fidelity molecular-modeling techniques. Quantum mechanical methods, such as ab initio wave-function (WF) theory, deliver the desired accuracy, but have impractical scaling for modeling biosystems with thousands of atoms. Combining molecular fragmentation with MP2 perturbation theory, this study presents an innovative approach that enables biomolecular-scale ab initio molecular dynamics (AIMD) simulations at WF theory level. Leveraging the resolution-of-the-identity approximation for Hartree-Fock and MP2 gradients, our approach eliminates computationally intensive four-center integrals and their gradients, while achieving near-peak performance on modern GPU architectures. The introduction of asynchronous time steps minimizes time step latency, overlapping computational phases and effectively mitigating load imbalances. Utilizing up to 9,400 nodes of Frontier and achieving 59% (1006.7 PFLOP/s) of its double-precision floating-point peak, our method enables us to break the million-electron and 1 EFLOP/s barriers for AIMD simulations with quantum accuracy. © 2024, CC BY-NC-ND.

关键词： Wave functions

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GyroTetris: Merging Mobile Gaming and Motion Sensing to Boost Motor Skills and Enjoyment

GyroTetris: Merging Mobile Gaming and Motion Sensing to Boos...

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Zilu Liang Shuma Nishida Takeru Noguchi Takuto Maenaka Ubiquitous and Personal Computing Lab Kyoto University of Advanced Science (KUAS) Kyoto Japan Faculty of Engineering Kyoto University of Advanced Science (KUAS) Kyoto Japan

ISBN: (数字)9798350355079

ISBN: (纸本)9798350355086

This paper presents the design, development, and initial evaluation of the GyroTetris game. This game combines classic Tetris gameplay with wrist movements to control Tetriminos, leveraging smartphone gyroscopes to enhance fine motor skills through engaging gameplay. An evaluation with 21 participants suggests that the game was perceived as enjoyable, simple, yet challenging. Our design replaces traditional button presses with a physical tilt or shake, adding an additional layer of physical engagement, which participants perceived as potentially beneficial to improve motor skills.

关键词： Wrist Training Presses Refining Merging Games Motors Sensors Gyroscopes Land mobile radio

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Utilization-Aware Data Variable Allocation on NVM- Based SPM in Real-Time Embedded Systems

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Journal of Electronic Science and Technology 2021年第2期19卷 163-172页

作者： Jin-Yu Zhan Yi-Xin Li Wei Jiang Jun-Huan Yang School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu 610054 State Key Lab of Mathematical Engineering and Advanced Computing Wuxi 214000

With the development of the nonvolatile memory(NVM),using NVM in the design of the cache and scratchpad memory(SPM)has been *** paper presents a data variable allocation(DVA)algorithm based on the genetic algorithm for NVM-based SPM to prolong the *** lifetime can be formulated indirectly as the write counts on each SPM *** the differences between global variables and stack variables,our optimization model has three *** constraints of the central processing unit(CPU)utilization and size are used for all variables,while no-overlay constraint is only used for stack *** satisfy the constraints of the optimization model,we use the greedy strategy to generate the initial population which can determine whether data variables are allocated to SPM and distribute them evenly on SPM ***,we use the Mälardalen worst case executive time(WCET)benchmark to evaluate our *** experimental results show that the DVA algorithm can not only obtain close-to-optimal solutions,but also prolong the lifetime by 9.17% on average compared with SRAM-based SPM.

关键词： Data variables allocation genetic algorithm nonvolatile momory(NVM) scratchpad memory(SPM)

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Learning to Localize Cross-Anatomy Landmarks in X-Ray Images with a Universal Model

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Biomedical Engineering Frontiers 2022年第1期3卷 298-308页

作者： Heqin Zhu Qingsong Yao Li Xiao S.Kevin Zhou Key Lab of Intelligent Information Processing of Chinese Academy of Sciences(CAS) Institute of Computing TechnologyCASBeijing 100190China Center for Medical Imaging RoboticsAnalytic Computing&Learning(MIRACLE)School of Biomedical Engineering&Suzhou Institute for Advanced ResearchUniversity of Science and Technology of ChinaSuzhou 215123China

Objective and Impact *** this work,we develop a universal anatomical landmark detection model which learns once from multiple datasets corresponding to different anatomical *** with the conventional model trained on a single dataset,this universal model not only is more light weighted and easier to train but also improves the accuracy of the anatomical landmark *** accurate and automatic localization of anatomical landmarks plays an essential role in medical image ***,recent deep learning-based methods only utilize limited data from a single *** is promising and desirable to build a model learned from different regions which harnesses the power of big *** model consists of a local network and a global network,which capture local features and global features,*** local network is a fully convolutional network built up with depth-wise separable convolutions,and the global network uses dilated convolution to enlarge the receptive field to model global *** evaluate our model on four 2D X-ray image datasets totaling 1710 images and 72 landmarks in four anatomical *** experimental results show that our model improves the detection accuracy compared to the state-of-the-art *** model makes the first attempt to train a single network on multiple datasets for landmark *** results qualitatively and quantitatively show that our proposed model performs better than other models trained on multiple datasets and even better than models trained on a single dataset separately.

关键词： convolution utilize separable

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Resource-aware scheduling of multiple quantum circuits on a hardware device

arXiv

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arXiv 2024年

作者： Bhoumik, Debasmita Majumdar, Ritajit Sur-Kolay, Susmita Advanced Computing & Microelectronics Unit Indian Statistical Institute India IBM Quantum IBM India Research Lab India

Recent quantum technologies and quantum error-correcting codes emphasize the requirement for arranging interacting qubits in a nearest-neighbor (NN) configuration while mapping a quantum circuit onto a given hardware device, in order to avoid undesirable noise. It is equally important to minimize the wastage of qubits in a quantum hardware device with m qubits while running circuits of n qubits in total, with n © 2024, CC BY.

关键词： Timing circuits

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Deep Learning Compiler Optimization on Multi-Chiplet Architecture

Deep Learning Compiler Optimization on Multi-Chiplet Archite...

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IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS)

作者： Huiqing Xu Kuang Mao Quihong Pan Zhaorong Tang Mengdi Wang Ying Wang Research Center for Advanced Computing Systems Zhejiang Lab Hangzhou China Institute of Computing Technology Chinese Academy of Sciences Beijing China

Multi-chiplet architecture can provide a high-performance solution for new tasks such as deep learning models. In order to fully utilize chiplets and accelerate the execution of deep learning models, we present a deep learning compilation optimization framework for chiplets, and propose a scheduling method based on data dependence. Experiments show that our method improves the compilation efficiency, and the performance of the scheduling scheme is at least 1-2 times higher than the traditional algorithms.

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Influence of electrochemical solution activation on kinetics of pyrite flotation: Experimental and theoretical investigations

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Journal of Industrial and Engineering Chemistry 2025年 148卷 791-796页

作者： Prokhorov, Konstantin V. Chibisova, Mary A. Srivastava, Anurag Mining Institute Far Eastern Branch of the Russian Academy of Sciences 51 Turgenev Str. Khabarovsk680000 Russia Computing Center Far Eastern Branch of the Russian Academy of Sciences 65 Kim Yu Chen Str. Khabarovsk680000 Russia Advanced Materials Research Group CNT Lab ABV - Indian Institute of Information Technology and Management Gwalior Madhya Pradesh 474015 India

This study modeled the molecular structure of adsorbed water films on the pyrite (100) surface using density functional theory (DFT) to investigate the effects of hydronium ions on the surface bonding interactions. Adding hydronium ions to the water films weakened the Fe–O interaction, increasing the Fe–O bond distance from 2.145 to 3.245 Å and, consequently, reducing hydrogen bonding among water molecules within a 5.5-Å range of the FeS2 surface. Molecular dynamics simulations further revealed the proton transfer dynamics from the subsurface to upper water layers, aligning with experimental observations of the kinetics of pyrite particle attachment to air bubbles. Electrochemical activation of a sodium bicarbonate solution enhanced the pyrite particle loading on the bubbles by up to 32 % compared to a nonactivated solution. These results suggest that hydronium-mediated surface modifications intensify the flotation process, providing insights into optimizing mineral separation techniques. © 2025

关键词： Molecular dynamics

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Blockchain-based Data Sharing Framework for Malaysia Government Aid Management System

Blockchain-based Data Sharing Framework for Malaysia Governm...

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2023 International Conference on Digital Applications, Transformation and Economy, ICDATE 2023

作者： Azmi, Azman Yahya, Farashazillah Moung, Ervin Gubin Sallehudin, Hasimi Utomo, Rio Guntur Azman, Nur Afrina Universiti Malaysia Sabah Advanced Machine Intelligence Research Lab Faculty of Computing and Informatics Sabah Kota Kinabalu Malaysia Universiti Malaysia Sabah Data Technologies and Application Research Lab Faculty of Computing and Informatics Sabah Kota Kinabalu Malaysia Universiti Kebangsaan Malaysia Centre for Software Technology and Management Faculty of Information Science and Technology Malaysia Telkom University School of Computing Bandung Indonesia Foundation Study and Language Institute University of Reading Malaysia Johor Iskandar Puteri Malaysia

ISBN: (纸本)9798350310689

The Covid-19 pandemic has impacted the world economy, and Malaysia is no exception. Due to the closure of businesses, the rate of unemployment has been recorded at an all-time high. The Malaysian Government through its agencies is assisting citizens through an online platform for incentives and an aid-giving platform. The current architecture of data sharing is using centralized, organization-oriented authentication and is not immutable for audit capability. In addition, the blockchain network is still a proof of concept in the digital government ecosystem. This paper studies the current aid data-sharing ecosystem and analyzes the potential of having a state government consortium blockchain framework that will be used in the Government Network Infrastructure for decentralized, transparent data sharing with audit capability. Several works involving Blockchain adoption based on domain, Blockchain consortium architecture and Government applications using blockchain are being presented. The need for a framework in implementing decentralized data sharing using consortium blockchain architecture for multi-agencies data sharing focusing on Malaysia Government Aid Management is discussed in this paper to provide the opportunity for the research domain. The findings in this paper will be used for future work on the requirement of a proposed government multi-layer consortium blockchain framework for data sharing in the Malaysian Government specifically for the Johore State Government ecosystem. © 2023 IEEE.

关键词： Blockchain

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Learning from Noisy Crowd labels with Logics 39

Learning from Noisy Crowd Labels with Logics

引用

39th IEEE International Conference on Data Engineering, ICDE 2023

作者： Chen, Zhijun Sun, Hailong He, Haoqian Chen, Pengpeng Beihang University Sklsde Lab Beijing China Beihang University Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing China China's Aviation System Engineering Research Institute Beijing China

ISBN: (纸本)9798350322279

This paper explores the integration of symbolic logic knowledge into deep neural networks for learning from noisy crowd labels. We introduce Logic-guided Learning from Noisy Crowd labels (Logic-LNCL), an EM-alike iterative logic knowledge distillation framework that learns from both noisy labeled data and logic rules of interest. Unlike traditional EM methods, our framework contains a "pseudo-E-step"that distills from the logic rules a new type of learning target, which is then used in the "pseudo-M-step"for training the classifier. Extensive evaluations on two real-world datasets for text sentiment classification and named entity recognition demonstrate that the proposed framework improves the state-of-the-art and provides a new solution to learning from noisy crowd labels. © 2023 IEEE.

关键词： Crowdsourcing

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