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检索条件"主题词=inference algorithms"

共 5352 条记录，以下是81-90 订阅

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Collaborative inference Acceleration with Non-Penetrative Tensor Partitioning

Collaborative Inference Acceleration with Non-Penetrative Te...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhibang Liu Chaonong Xu Zhenjie Lv Zhizhuo Liu Suyu Zhao Beijing Key Lab of Petroleum Data Mining China University of Petroleum Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The inference of large-sized images on Internet of Things (IoT) devices is commonly hindered by limited resources, while there are often stringent latency requirements for Deep Neural Network (DNN) inference. Currently, this problem is generally addressed by collaborative inference, where the large-sized image is partitioned into multiple tiles, and each tile is assigned to an IoT device for processing. However, since significant latency will be incurred due to the communication overhead caused by tile sharing, the existing collaborative inference strategy is inefficient for convolutional computation, which is indispensable for any DNN. To reduce it, we propose Non-Penetrative Tensor Partitioning (NPTP), a fine-grained tensor partitioning method that reduces the communication latency by minimizing the communication load of tiles shared, thereby reducing inference latency. We evaluate NPTP with four widely-adopted DNN models. Experimental results demonstrate that NPTP achieves a 1.44-1.68× inference speedup relative to CoEdge, a state-of-the-art (SOTA) collaborative inference algorithm.

关键词： Tensors Convolution Collaboration Signal processing algorithms Artificial neural networks inference algorithms Partitioning algorithms Internet of Things Speech processing Load modeling

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Adaptive Neuro Fuzzy inference System for Programmable Logic Controller

Adaptive Neuro Fuzzy Inference System for Programmable Logic...

引用

Cybernetics & Informatics (K&I)

作者： Ladislav Körösi Jana Paulusová Oliver Halaš Institute of Robotics and Cybernetics Slovak University of Technology Ilkovičova 3 Bratislava Slovak Republic

ISBN: (数字)9798331541811

ISBN: (纸本)9798331541828

The aim of this paper is to present an Adaptive Neuro-Fuzzy inference System (ANFIS) for implementation on a Programmable Logic Controller (PLC) within the TIA Portal. ANFIS and fuzzy systems are not included in the standard PLC library, highlighting the importance of designing these systems independently. The ANFIS design is developed in MATLAB after which its structure is exported as a Data Block (DB) to the PLC, where a developed algorithm processes the imported structure. The ANFIS can serve various roles, such as a model or controller, depending on the designer’s objectives. The functionality of the proposed approach will be demonstrated through examples in MATLAB and on the PLC.

关键词： Fuzzy logic Adaptive systems Industrial control Computational modeling Programmable logic devices Mathematical models inference algorithms MATLAB Portals Interoperability

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Flexible Event-Driven Biological Imaging via Bayesian inference

Flexible Event-Driven Biological Imaging via Bayesian Infere...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yayin Tan Tianle Zhao Zhiqin Chu Department of Electrical and Electronic Engineering the University of Hong Kong Department of Electronic Engineering The Chinese University of Hong Kong

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Event-driven imaging is gaining more and more attention in the field of biological imaging, where conventional frame-based imaging techniques confront with the bottleneck of capturing weak and fast-changing biological signals. However, existing methods for processing event camera signals, such as detection, de-noising, etc, are mainly designed for computer vision problems like tracking and surveillance and do not well adapts to biological applications. Particularly, a typical biological imaging system focuses on static scenes with fast-changing and weak illumination. There lacks effective detection algorithms under this extreme lighting condition. To this end, we proposed a novel framework for recovering the weak biological signals from the noisy event camera measurements. Based on Bayesian inference, the proposed approach offers a flexible solution for diverse imaging modalities. We validated our method on real-world biological imaging experiments, demonstrating its robustness and effectiveness of detecting weak and fast varying targets. This unlocks new possibilities for weak biological signal detection under extreme lighting conditions in a variety of imaging applications.

关键词： Surveillance Lighting Signal processing algorithms Modulation Cameras Biology inference algorithms Bayes methods Noise measurement Speech processing

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DynamicAttention: Dynamic KV Cache for Disaggregate LLM inference

DynamicAttention: Dynamic KV Cache for Disaggregate LLM Infe...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhiqiang Ding Tongkai Yang Platform Technology Business Unit Ant Group HangZhou China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In large language model inference, efficient utilization of GPU memory is of utmost importance. Current systems suffer from unreasonable GPU memory allocation: excessive memory is idle under low load, while memory shortages occur under high load. Moreover, existing GPU memory management methods do not consider the different memory requirements for prefill and decode stages in the inference process. This paper proposes a new solution - DynamicAttention. It allocates a continuous virtual GPU memory space at startup, but does not actually allocate physical GPU memory. Instead, it dynamically allocates memory according to the demand during runtime. Different memory consumption prediction algorithms are designed specifically for the Prefill and Decode stages. Experimental results show that compared with the existing SOTA solutions, DynamicAttention improves GPU memory utilization by 4 times under low load and 15% under high load. Furthermore, it increases throughput by 1.6 times.

关键词： Runtime Heuristic algorithms Memory management Graphics processing units Signal processing algorithms Throughput Prediction algorithms inference algorithms Resource management Load modeling

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Divide-and-Conquer Variational Bayesian inference for Multi-task Learning of High-resolution SAR Imagery

Divide-and-Conquer Variational Bayesian Inference for Multi-...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Lei Yang Ming Sun Zhongwei Hu Zenan Zhang Wenxuan Yuan College of Electronic Information and Automation Civil Aviation University of China Tianjin China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Conventional statistical-driven synthetic aperture radar (SAR) imaging algorithms can only encode a single and/or static prior, leading to that limited features can be accessed quantitatively. To this end, a novel multi-task learning framework is proposed by devising a divide-and-conquer variational Bayesian (DC-VB) inference, so that elaborated features of interests can be exploited for a high-resolution SAR imagery. Specifically, a flexible generalized Gaussian distribution (GGD) and a customized hybrid probability distribution are introduced and employed for the priors of features of interests. To resolve the resultant complicated Bayesian inference, splitted random variables are incorporated, so that the joint posterior problem can be decomposed into multiple local problems that are easy to be solved. Simultaneously, dual variables are established for residual errors of the decomposition. To guarantee a global solution for the image of the target of interests, the data augmentation is employed to coordinate multiple local solutions. Therefore, the intended Bayesian inference works in a divide-and-conquer manner, which is superior in quantization of multiple features of high-resolution SAR imagery. It is capable of incorporating multiple priors in a fully-statistical probability and guaranteeing closed-form solutions of posterior distributions. Unavoidable propagation errors can be minimized in the DC-VB process. Raw SAR data is applied to validate the effectiveness of the proposed algorithm. Comparisons with conventions show the superiority in terms of qualitative and quantitative aspects.

关键词： Quantization (signal) Closed-form solutions Signal processing algorithms Multitasking Data augmentation Radar polarimetry inference algorithms Bayes methods Speech processing Synthetic aperture radar

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Multi Modal English Vocabulary inference Algorithm Integrating ELECTRA and Knowledge Graph

Multi Modal English Vocabulary Inference Algorithm Integrati...

引用

Cybersecurity, Internet of Things and Soft Computing (CITSC), Asia-Europe Conference on

作者： Qinyue Wu Technical College for the Deaf Tianjin University of Technology Tianjin China

ISBN: (数字)9798331504205

ISBN: (纸本)9798331504212

In current English learning, the improvement of vocabulary inference ability is crucial to learners” language application ability. However, the existing single-mode algorithms are often unable to fully tap the deep relationship between words, resulting in insufficient accuracy of inference. This article aims to propose a multimodal English vocabulary inference algorithm that integrates ELECTRA model and knowledge graph to improve the accuracy and efficiency of vocabulary inference. Firstly, the ELECTRA model is used for deep learning of contextual semantics, and then a knowledge graph based vocabulary relationship network is constructed to integrate the semantic and relational information of vocabulary. Next, by integrating the contextual representation of ELECTRA with the node features of the knowledge graph through multimodal feature fusion technology, an inference model is constructed for training and optimization. The experimental results show that the algorithm achieves an accuracy of 87.5% in vocabulary inference tasks, which is more than 8% higher than traditional methods, and also improves inference efficiency by 25%. The multimodal inference algorithm that integrates ELECTRA and knowledge graph effectively solves the problem of information silos in vocabulary inference, providing English learners with more accurate and efficient vocabulary learning tools.

关键词： Deep learning Training Vocabulary Accuracy Semantics Knowledge graphs inference algorithms Internet of Things Optimization Context modeling

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Online Network inference from Graph-Stationary Signals with Hidden Nodes

Online Network Inference from Graph-Stationary Signals with ...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Andrei Buciulea Madeline Navarro Samuel Rey Santiago Segarra Antonio G. Marques Dept. of Signal Theory and Communications Rey Juan Carlos University Madrid Spain Dept. of Electrical and Computer Engineering Rice University Houston TX USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Graph learning is the fundamental task of estimating unknown graph connectivity from available data. Typical approaches assume that not only is all information available simultaneously but also that all nodes can be observed. However, in many real-world scenarios, data can neither be known completely nor obtained all at once. We present a novel method for online graph estimation that accounts for the presence of hidden nodes. We consider signals that are stationary on the underlying graph, which provides a model for the unknown connections to hidden nodes. We then formulate a convex optimization problem for graph learning from streaming, incomplete graph signals. We solve the proposed problem through an efficient proximal gradient algorithm that can run in real-time as data arrives sequentially. Additionally, we provide theoretical conditions under which our online algorithm is similar to batch-wise solutions. Through experimental results on synthetic and real-world data, we demonstrate the viability of our approach for online graph learning in the presence of missing observations.

关键词： Heuristic algorithms Signal processing algorithms Estimation Signal processing Real-time systems inference algorithms Convex functions Acoustics Speech processing Optimization

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algorithms for inference in specialized maps for recovering 3D hand pose

Algorithms for inference in specialized maps for recovering ...

引用

5th IEEE International Conference on Automatic Face and Gesture Recognition

作者： Rosales, R Sclaroff, S Boston Univ Dept Comp Sci Boston MA 02215 USA

ISBN: (纸本)0769516025

Learning in the Specialized Mappings Architecture (SMA) was presented for recovering 3D hand pose from visual features, however inference was not fully addressed. In this paper we tackle this aspect of the SMA model more thoroughly, and propose two inference algorithms, one deterministic and one probabilistic. SMA consists of several specialized forward (input to output) mapping functions that are estimated automatically from data and a known feedback or inverse function (that maps outputs to inputs). This allows the use of alternative conditional independence assumptions in the same model (derived from a forward and a feedback model) during learning and inference. The two proposed inference approximations, the mean output (MO) and the multiple sampling (MS) algorithms, are tested in recovering 3D hand pose from single images.

关键词： inference algorithms Computer science Output feedback Image sampling System testing Machine vision Biological system modeling Humans Object detection Skin

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algorithms for the inference of Causality in Dynamic Processes: Application to Cardiovascular and Cerebrovascular Variability 37

Algorithms for the Inference of Causality in Dynamic Process...

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37th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Faes, Luca Porta, Alberto Nollo, Giandomenico Univ Trento IRCS FBK Dept Ind Engn Trento Italy Univ Trento BIOtech Dept Ind Engn Trento Italy Univ Milan Dept Biomed Sci Hlth Milan Italy IRCCS Policlin San Donato Dept Cardiothorac Vasc Anesthesia & Intens Care Milan Italy

ISBN: (纸本)9781424492701

This study faces the problem of causal inference in multivariate dynamic processes, with specific regard to the detection of instantaneous and time-lagged directed interactions. We point out the limitations of the traditional Granger causality analysis, showing that it leads to false detection of causality when instantaneous and time-lagged effects coexist in the process structure. Then, we propose an improved algorithm for causal inference that combines the Granger framework with the approach proposed by Pearl for the study of causality among multiple random variables. This new approach is compared with the traditional one in theoretical and simulated examples of interacting processes, showing its ability to retrieve the correct structure of instantaneous and time-lagged interactions. These approaches for causal inference are then tested on the physiological variability series of heart period, arterial pressure and cerebral blood flow variability obtained in subjects with postural-related syncope during a tilt-test protocol.

关键词： blood blood vessels brain cardiovascular system haemodynamics random processes Pearl method arterial pressure cardiovascular variability causality inference algorithms cerebral blood flow variability cerebrovascular variability heart period instantaneous directed interactions multiple random variables multivariate dynamic processes physiological variability series postural-related syncope tilt-test protocol time-lagged directed interactions traditional Granger causality analysis Algorithm design and analysis Blood flow Heuristic algorithms inference algorithms Physiology Random variables Time series analysis

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algorithms and complexity results for #SAT and Bayesian inference 44

Algorithms and complexity results for #SAT and Bayesian infe...

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44th Annual IEEE Symposium on Foundations of Computer Science

作者： Bacchus, F Dalmao, S Pitassi, T Univ Toronto Dept Comp Sci Toronto ON M5S 3G4 Canada

ISBN: (纸本)0769520405

Bayesian inference is an important problem with numerous applications in probabilistic reasoning. Counting satisfying assignments is a closely related problem of fundamental theoretical importance. In this paper we show that plain old DPLL equipped with memoization (an algorithm we call #DPLLCache) can solve both of these problems with time complexity that is at least as good as state-of-the-art exact algorithms, and that it cat, also achieve the best known time-space tradeoff. We then proceed to show that there are instances where #DPLLCache can achieve an exponential speedup over existing algorithms.

关键词： Application software Bayesian methods Computational modeling Computer science Computer simulation Government inference algorithms Polynomials

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