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A Survey of Non-Volatile Main Memory File systems

Journal of Computer Science & Technology 2023年第2期38卷 348-372页

作者：王盈贾文庆蒋德钧熊劲 State Key Laboratory of Processors Institute of Computing TechnologyChinese Academy of Sciences Beijing 100190China Research Center for Advanced Computer Systems Institute of Computing TechnologyChinese Academy of Sciences Beijing 100190China University of Chinese Academy of Sciences Beijing 100049China

Non-volatile memories(NVMs)provide lower latency and higher bandwidth than block ***,NVMs are byte-addressable and provide persistence that can be used as memory-level storage devices(non-volatile main memory,NVMM).These features change storage hierarchy and allow CPU to access persistent data using load/store ***,we can directly build a file system on ***,traditional file systems are designed based on slow block *** use a deep and complex software stack to optimize file system *** design results in software overhead being the dominant factor affecting NVMM file ***,scalability,crash consistency,data protection,and cross-media storage should be reconsidered in NVMM file *** survey existing work on optimizing NVMM file ***,we analyze the problems when directly using traditional file systems on NVMM,including heavy software overhead,limited scalability,inappropriate consistency guarantee techniques,***,we summarize the technique of 30 typical NVMM file systems and analyze their advantages and ***,we provide a few suggestions for designing a high-performance NVMM file system based on real hardware Optane DC persistent memory ***,we suggest applying various techniques to reduce software overheads,improving the scalability of virtual file system(VFS),adopting highly-concurrent data structures(e.g.,lock and index),using memory protection keys(MPK)for data protection,and carefully designing data placement/migration for cross-media file system.

关键词： non-volatile main memory(NVMM) file system performance scalability crash consistency data protection crossmeida

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Hybrid EEG-fNIRS Detection of MCI Subtypes Based on Transformer Network

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SN Computer Science 2025年第4期6卷 1-13页

作者： Bouaziz, Bassem Chaabene, Siwar Mahdi, Walid Multimedia InfoRmation Systems and Advanced Computing Laboratory (MIRACL) University of Sfax Sfax 3023 Tunisia

Preventing the progression of mild cognitive impairment (MCI) to dementia is of paramount importance in the field of healthcare. Furthermore, detecting the subtle indications of MCI in its early stages is difficult but necessary. MCI can appear as either amnestic (aMCI) or non-amnestic (naMCI), confounding the diagnosis even more. However, with the development of improved neuroimaging techniques, computational algorithms that analyze data acquired during clinical tests might provide a solution. Among them, functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) offer invaluable insights into an individual’s cognitive state. In this paper, we present a novel approach for distinguishing individuals diagnosed with MCI, specifically aMCI and naMCI, from healthy controls (HC). We employ a hybrid fusion of EEG and fNIRS signals, acquired during a cognitive task. To address this complex classification problem, we propose a new method based on the transformer network for MCI detection. Our experimental results demonstrate a remarkable classification accuracy of 99.78%, highlighting the significant improvement in MCI detection capabilities achieved through the integration of multimodal neuroimaging data. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cognitive task EEG FNIRS HC vs aMCI vs naMCI MCI detection Multi-modality Transformer network

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Deep Learning for Detecting Mental Disorder from User Content on Social Media

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SN Computer Science 2025年第2期6卷 1-29页

作者： Tounsi, Malek Ameur, Hanen Jamoussi, Salma Multimedia Information Systems and Advanced Computing Laboratory University of Sfax Technopole of Sfax Sfax Tunisia

The prevalence of mental health disorders is rising, with many individuals expressing their thoughts and feelings through social media posts. This presents an opportunity for early detection of mental conditions using natural language processing (NLP). This paper addresses the lack of effective tools for detecting mental disorders from Arabic social media content. We propose Ara-MentalBERT, a novel deep learning fusion model that combines AraBERT and MentalBERT using a deep joint autoencoder (DJAE). The DJAE model aims to integrate Arabic language understanding with specialized mental health domain knowledge by fusing Arabic and mental health embedding representations of text. Our proposed method involves three key steps: (1) fine-tuning pre-trained AraBERT and MentalBERT models, (2) employing a deep joint autoencoder for Ara-Mental text representation learning, and (3) utilizing a mental health disorder classifier with the fused Ara-Mental representations. We evaluate our model on two diverse datasets: a Reddit dataset and a Twitter dataset. Results demonstrate significant improvements over existing methods, achieving an accuracy of 81.12% on the Reddit dataset for combined titles and posts and 86.12% accuracy on the Arabic tweets. These findings highlight the potential of our model to enhance mental health detection in Arabic-speaking communities, offering a powerful tool for identifying at-risk individuals more effectively. Our proposed method makes valuable progress at the intersection of NLP, mental health, and deep learning, paving the way for Arabic social media monitoring to facilitate early intervention and improve mental healthcare. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ara-Mental representation Ara-MentalBERT AraBERT Deep joint autoencoder Deep learning Mental health disorders MentalBERT

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Towards Scalability and Performance: Framework for Heterogeneous Cluster Integration in Deep Learning Accelerators 4

Towards Scalability and Performance: Framework for Heterogen...

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4th IEEE International Conference on VLSI systems, Architecture, Technology and Applications, VLSI SATA 2024

作者： Bhagirath, K. Pant, Dheeraj Tiwari, Abhishek Khaneja, Vivek Centre For Development Of Advanced Computing Embedded Systems Division Noida India

ISBN: (纸本)9798350362268

This paper presents a streamlined approach to address the challenges of integrating heterogeneous clusters in deep learning(DL) accelerators. As the demand for scalable and high-performance computing in DL continues to grow, the proposed framework offers an easy implementation solution, ensuring efficiency without compromise. The method details core components and design principles, guiding the inclusion of heterogeneous clusters step by step. Extensive experiments show the framework's adaptability, handling diverse workloads and cluster architectures. Our exploration integrates various dataflows, dynamically selected by NN during runtime for optimal performance. Results indicate improved scalability and robustness compared to existing methods. This research provides a valuable resource for practitioners seeking efficient and accessible solutions for harnessing the power of heterogeneous clusters in DL acceleration, with implications for advancements in AI and computational efficiency. © 2024 IEEE.

关键词： Computational efficiency

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Emulation of Recommendation System Using QSVT Framework of QiML on CPU 4th

Emulation of Recommendation System Using QSVT Framework of...

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4th International Conference on Emerging Trends and Technologies on Intelligent systems, ETTIS 2024

作者： Patil, Akshay Tiwari, Abhishek Khaneja, Vivek Singh, Nagendra Embedded Systems R&D Centre for Development of Advanced Computing Noida India

ISBN: (纸本)9789819757022

Classical computing (CC) takes place in the binary form of 0 and 1, whereas Quantum computing (QC) takes place adhering to fundamental principles of quantum mechanics, i.e., superposition, entanglement, and tunneling. Humanity is in the race to show that QC is better than CC in terms of time, accuracy, and memory requirements. But since Fault-Tolerant QC hardware with solving problems computationally exhaustive for today’s CC are out of reach, we in this paper emulate Use case of the Recommendation System on Quantum Singular Value Transform Framework (QSVT) of Quantum-Inspired Machine Learning (QiML) on CPU using the Qiskit Library, pyqsp library and have shown a quantum advantage in terms of accuracy, in terms of time and memory requirement classical implementation is a winner as computations in the Quantum Emulations are larger owing to unitary matrix conversions of input matrix and considerable depths, but these depths can be reduced to approximate the target polynomial function in which case the Quantum Implementation is comparable to/better than its counterpart at cost of *** entanglement principle of QM is used in the QSVT Circuit for entangling the QSVT Qubit with unitary Qubits by using the CNOT gates. Also, the entanglement principle is inherently present in the Input User Preference Rating Unitary Matrix as well, if the exact tensor decomposition at a break-off qubit length of the input user preference unitary matrix is not *** input quantum state of our QSVT circuit can be initialized to the superposition of input user states to access all the items corresponding to all the users in one run of the circuit. Hence by using the fundamental principles of quantum mechanics, we have shown quantum advantage over classical implementation in terms of accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Qubits

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Parameter-efficient fine-tuning of Whisper for low-resource speech recognition 5

Parameter-efficient fine-tuning of Whisper for low-resource ...

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5th International Seminar on Artificial Intelligence, Networking and Information Technology, AINIT 2024

作者： Liu, Yunpeng Qu, Dan School of Information Systems Engineering University of Information Engineering Laboratory For Advanced Computing and Intelligence Engineering Department of Artificial Intelligence Henan China Systems Engineering University of Information Engineering Laboratory For Advanced Computing and Intelligence Engineering Henan China

ISBN: (纸本)9798350385557

Limited data availability remains a significant challenge for Whisper's low-resource speech recognition performance, falling short of practical application requirements. While previous studies have successfully reduced the recognition error rates of target language speech through fine-tuning, a comprehensive exploration and analysis of Whisper's parameter-efficient fine-tuning capabilities, the advantages and disadvantages are still lacking. This paper aims to fill this gap by conducting comprehensive experimental exploration for Whisper's low-resource speech recognition performance using two parameter-efficient fine-tuning strategies with limited supervised data from multiple low-resource languages. The results and analysis demonstrate that all fine-tuning strategies explored in this paper significantly enhance Whisper's performance. However, different strategies vary in their suitability and practical effectiveness, highlighting the need for careful selection based on specific use cases and resources available. © 2024 IEEE.

关键词： Speech recognition

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Federated Machine Learning Architecture for Heterogeneous Quantum Devices 1

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3rd International Conference on "Emerging Trends and Technologies on Intelligent systems" (ETTIS-2023)

作者： Singh, Nagendra Tiwari, Abhishek Khaneja, Vivek Embedded Systems Division Centre for Development of Advanced Computing Noida India

ISBN: (数字)9789819939633

ISBN: (纸本)9789819939626

Quantum computing shows immense potential for solving traditional, near to impossible, complex problems. It also shows how the performance and speed can be improved for the solution of existing classical problems by using quantum mechanics. However, the computation power of current computers, servers, and supercomputers is well enough to speed up the existing problem solutions. But as data generated from various resources increases rapidly, in current scenarios more than 2.5 quintillion bytes of data is generated daily and is expected to be 200+ zettabytes on the cloud by 2025. Handling and storing such a massive amount of data is a significant issue for current computing and storage devices. Quantum computing has the capability to resolve this issue with quantum rules. In this paper, the task of pattern finding and recognizing in data (i.e., machine learning) is explored with the help of quantum computing and introducing a collaborative learning architecture. It enables collaborative computation from heterogeneous quantum computing devices for machine learning tasks. Also, it can be adapted to any quantum machine learning approach and speed up the learning phase with better data privacy. The proposed scheme is implemented with the MNIST dataset to compare with current existing research work. Results show that the proposed architecture matches existing research work’s performance with the additional features of privacy and heterogeneity of quantum devices. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Machine learning

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***: Leveraging HPC Metaprogramming and Performance Portability for Computations That Use Shared Memory GPUs

***: Leveraging HPC Metaprogramming and Performance Portabil...

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2024 IEEE High Performance Extreme computing Conference, HPEC 2024

作者： Valero-Lara, Pedro Godoy, William F. Teranishi, Keita Vetter, Jeffrey S. Advanced Computing Systems Research Section Oak Ridge National Laboratory Oak RidgeTN United States

ISBN: (纸本)9798350387131

In this work, we present ***, a new feature of Julia for ACCelerators (JACC), which is the performanceportable and metaprogramming model of the just-in-time and LLVM-based Julia language. This new feature allows JACC applications to leverage the high-performance computing (HPC) capabilities of high-bandwidth, on-chip GPU memory. Historically, exploiting high-bandwidth, shared-memory GPUs has not been a priority for high-level programming solutions. *** covers that gap for the first time, thereby providing a highlevel, portable, and easy-to-use solution for programmers to exploit this memory and supporting all current major accelerator architectures. Well-known HPC and AI workloads, such as multi/hyperspectral imaging and AI convolutions, have been used to evaluate *** on two exascale GPU architectures hosted by some of the most powerful US Department of Energy supercomputers: Perlmutter (NVIDIA A100) and Frontier (AMD MI250X). The performance evaluation reports speedup of up to 3.5 × by adding only one line of code to the base codes, thus providing important accelerators in a simple, portable, and transparent way and elevating the programming productivity and performance-portability capabilities for Julia/JACC HPC, AI, and scientific applications. © 2024 IEEE.

关键词： Memory architecture

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Meta-Adapter for Self-Supervised Speech Models: A Solution to Low-Resource Speech Recognition Challenges 30

Meta-Adapter for Self-Supervised Speech Models: A Solution t...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Chen, Yaqi Zhang, Hao Yang, Xukui Zhang, Wenlin Qu, Dan School of Information Systems Engineering Information Engineering University China Laboratory for Advanced Computing and Intelligence Engineering China

ISBN: (纸本)9782493814104

Self-supervised models have demonstrated remarkable performance in speech processing by learning latent representations from large amounts of unlabeled data. Although these models yield promising results on low-resource languages, the computational expense of fine-tuning all model parameters is prohibitively high. Adapters offer a solution by incorporating lightweight bottleneck structures into pre-trained models, enabling efficient parameter adaptation for downstream tasks. However, randomly initialized adapters often underperform in low-resource scenarios, limiting their applicability in low-resource languages. To address this issue, we develop the Meta-Adapter for self-supervised models to obtain meta-initialized parameters that facilitate quick adaptation to low-resource languages. Extensive experiments on the Common Voice and FLEURS datasets demonstrate the superior performance of Meta-Adapters on 12 low-resource languages spanning four different language families. Moreover, Meta-adapters show better generalization and extensibility than traditional pretraining methods. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Speech processing

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Detection of Heart Diseases Using CNN-LSTM 22nd

Detection of Heart Diseases Using CNN-LSTM

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22nd International Conference on Hybrid Intelligent systems, HIS 2022 and the 18th International Conference on Information Assurance and Security, IAS 2022

作者： Karoui, Hend Hamza, Sihem Ayed, Yassine Ben Multimedia Information Systems and Advanced Computing Laboratory MIRACL University of Sfax Sfax Tunisia

ISBN: (纸本)9783031274084

The ElectroCardioGram (ECG) is one of the most used signals for the prediction of heart disease. Lots of research are based on the ECG signal for the detection of cardiac diseases. In this research, we propose a four phases method to the recognition of cardiac disease. The first phase is to remove noise and detect QRS complex using a pass-band filter. In the second phase, we segment the filtered signal. For third phase is to make a fusion of three types of characteristics such as Zero Crossing Rate (ZCR), entropy and cepstral coefficients. Those features extracted considered as input in the next step which is the combination between Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) proposed for multi-class classification of the ECG signal into five classes: Normal beat (N), Left bundle-branch block beat (L), Right bundle-branch block beat (R), premature Ventricular contraction (V), and Paced beat (P). The proposed model was evaluated using the MITBIH arrhythmia database and achieve an accuracy equal to 95.80%. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Long short-term memory

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