The flourish of deep learning frameworks and hardware platforms has been demanding an efficient compiler that can shield the diversity in both software and hardware in order to provide application *** the existing dee...
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The flourish of deep learning frameworks and hardware platforms has been demanding an efficient compiler that can shield the diversity in both software and hardware in order to provide application *** the existing deep learning compilers,TVM is well known for its efficiency in code generation and optimization across diverse hardware *** the meanwhile,the Sunway many-core processor renders itself as a competitive candidate for its attractive computational power in both scientific computing and deep learning *** paper combines the trends in these two ***,we propose swTVM that extends the original TVM to support ahead-of-time compilation for architecture requiring cross-compilation such as *** addition,we leverage the architecture features during the compilation such as core group for massive parallelism,DMA for high bandwidth memory transfer and local device memory for data locality,in order to generate efficient codes for deep learning workloads on *** experiment results show that the codes generated by swTVM achieve 1.79x improvement of inference latency on average compared to the state-of-the-art deep learning framework on Sunway,across eight representative *** work is the first attempt from the compiler perspective to bridge the gap of deep learning and Sunway processor particularly with productivity and efficiency in *** believe this work will encourage more people to embrace the power of deep learning and Sunwaymany-coreprocessor.
At present,there is a problem of false positives caused by the too vast mimic scope in mimic transformation *** studies have focused on the“compensation”method to deal with this problem,which is expensive and cannot...
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At present,there is a problem of false positives caused by the too vast mimic scope in mimic transformation *** studies have focused on the“compensation”method to deal with this problem,which is expensive and cannot fundamentally solve *** paper provides new insights into coping with the ***,this study summarizes the false-positive problem in the mimic transformation,analyzes its possible harm and the root ***,three properties about the mimic scope are *** on the three properties and security quantification technology,the best mimic component set theory is put forward to solve the false-positive *** are two algorithms,the supplemental method and the subtraction *** best mimic component set obtained by these two algorithms can fundamentally solve the mimic system’s false-positive problem but reduce the cost of mimic *** make up for the lack of previous researches.
Data-driven machine learning(ML) is widely employed in the analysis of materials structure-activity relationships,performance optimization and materials design due to its superior ability to reveal latent data pattern...
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Data-driven machine learning(ML) is widely employed in the analysis of materials structure-activity relationships,performance optimization and materials design due to its superior ability to reveal latent data patterns and make accurate ***,because of the laborious process of materials data acquisition,ML models encounter the issue of the mismatch between a high dimension of feature space and a small sample size(for traditional ML models) or the mismatch between model parameters and sample size(for deep-learning models),usually resulting in terrible ***,we review the efforts for tackling this issue via feature reduction,sample augmentation and specific ML approaches,and show that the balance between the number of samples and features or model parameters should attract great attention during data quantity *** this,we propose a synergistic data quantity governance flow with the incorporation of materials domain *** summarizing the approaches to incorporating materials domain knowledge into the process of ML,we provide examples of incorporating domain knowledge into governance schemes to demonstrate the advantages of the approach and *** work paves the way for obtaining the required high-quality data to accelerate materials design and discovery based on ML.
This paper proposes a super-resolution (SR) reconstruction method to improve the poor quality of low-resolution (LR) computed tomography (CT) images. Transformer-based methods have shown promising performance in image...
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While Reconfigurable Intelligent Surfaces (RIS) have been shown to enhance OFDM communication performance, this paper unveils a potential security concern arising from widespread RIS deployment. Malicious actors could...
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The coprime array constructs virtual array to obtain higher degrees of freedom (DOF), but the premise of doing so is that the signals are independent of each other. Once there are coherent signals, the signal model of...
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The majority of existing Wi-Fi backscatter systems transmit tag data at rates lower than 250 kbps, as the tag data is modulated at OFDM symbol level, allowing for demodulation using commercial Wi-Fi receivers. However...
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Automatic diagnosis and monitoring of Alzheimer’s Disease (AD) can have a significant impact on society as well as the well-being of patients. It is known that Alzheimer’s disease (AD) influences the language abilit...
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Parkinson’s disease (PD) is a neurodegenerative disease ranked second after Alzheimer’s disease. It affects the central nervous system and causes a progressive and irreversible loss of neurons in the dopaminergic sy...
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The structural modeling of open-high-low-close(OHLC)data contained within the candlestick chart is crucial to financial ***,the inherent constraints in OHLC data pose immense challenges to its structural *** that fail...
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The structural modeling of open-high-low-close(OHLC)data contained within the candlestick chart is crucial to financial ***,the inherent constraints in OHLC data pose immense challenges to its structural *** that fail to process these constraints may yield results deviating from those of the original OHLC data *** address this issue,a novel unconstrained transformation method,along with its explicit inverse transformation,is proposed to properly handle the inherent constraints of OHLC data.A flexible and effective framework for structurally modeling OHLC data is designed,and the detailed procedure for modeling OHLC data through the vector autoregression and vector error correction model are provided as an example of multivariate time-series *** simulations and three authentic financial datasets from the Kweichow Moutai,CSI 100 index,and 50 ETF of the Chinese stock market demonstrate the effectiveness and stability of the proposed modeling *** modeling results of support vector regression provide further evidence that the proposed unconstrained transformation not only ensures structural forecasting of OHLC data but also is an effective feature-extraction method that can effectively improve the forecasting accuracy of machine-learning models for close prices.
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