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检索条件"任意字段=15th Symposium on Computer Architecture and High Performance Computing"

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Proceedings - 2024 IEEE International symposium on high-performance computer architecture, HPCA 2024

Proceedings - 2024 IEEE International Symposium on High-Perf...

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30th IEEE International symposium on high-performance computer architecture, HPCA 2024

ISBN: (纸本)9798350393132

the proceedings contain 78 papers. the topics discussed include: exploitation of security vulnerability on retirement;GadgetSpinner: a new transient execution primitive using the loop stream detector;uncovering and exploiting AMD speculative memory access predictors for fun and profit;Revet: a language and compiler for dataflow threads;an optimizing framework on MLIR for efficient FPGA-based accelerator generation;Celeritas: out-of-core based unsupervised graph neural network via cross-layer computing 2024;MEGA: a memory-efficient GNN accelerator exploiting degree-aware mixed-precision quantization;Gemini: mapping and architecture co-exploration for large-scale DNN Chiplet accelerators;STELLAR: energy-efficient and low-latency SNN algorithm and hardware co-design with spatiotemporal computation;and MIMDRAM: an end-to-end processing-using-DRAM system for high-throughput, energy-efficient and programmer-transparent multiple-instruction multiple-data computing.

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HotTiles: Accelerating SpMM with Heterogeneous Accelerator architectures 30

HotTiles: Accelerating SpMM with Heterogeneous Accelerator A...

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30th IEEE International symposium on high-performance computer architecture (HPCA)

作者： Gerogiannis, Gerasimos Aananthakrishnan, Sriram Torrellas, Josep Hur, Ibrahim Intel Corp Santa Clara CA 95051 USA Univ Illinois Urbana IL 61801 USA

ISBN: (纸本)9798350393132;9798350393149

Sparse Matrix Dense Matrix Multiplication (SpMM) is an important kernel with application across a wide range of domains, including machine learning and linear algebra solvers. In many sparse matrices, the pattern of nonzeros is nonuniform: nonzeros form dense and sparse regions, rather than being uniformly distributed across the whole matrix. We refer to this property as Intra-Matrix Heterogeneity (IMH). Currently, SpMM accelerator designs do not leverage this heterogeneity. they employ the same processing elements (PEs) for all the regions of a sparse matrix, resulting in suboptimal acceleration. To address this limitation, we utilize heterogeneous SpMM accelerator architectures, which include different types of PEs to exploit IMH. We develop an analytical modeling framework to predict the performance of different types of accelerator PEs taking into account IMH. Furthermore, we present a heuristic for partitioning sparse matrices among heterogeneous PEs. We call our matrix modeling and partitioning method HotTiles. To evaluate HotTiles, we simulate three different heterogeneous architectures. Each one consists of two types of workers (i.e., PEs): one suited for compute-bound denser regions (Hot Worker) and one for memory-bound sparser regions (Cold Worker). Our results show that exploiting IMH with HotTiles is very effective. Depending on the architecture, heterogeneous execution with HotTiles outperforms homogeneous execution using only hot or only cold workers by 9.2-16.8x and 1.4-3.7x, respectively. In addition, HotTiles outperforms the best worker type used on a per-matrix basis by 1.3-2.5x. Finally, HotTiles outperforms an IMH-unaware heterogeneous execution strategy by 1.4-2.2x.

关键词： hardware accelerators heterogeneous computing sparse computations SpMM

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POD: PCM-Based computing Platform for Object Detection in Biomedical Imaging Application 15

POD: PCM-Based Computing Platform for Object Detection in Bi...

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15th Latin American symposium on Circuits and Systems (LASCAS)

作者： Chen, Demeng Amirsoleimani, Amirali Azghadi, Mostafa Rahimi Genov, Roman Ahmadi, Majid Univ Toronto Dept Elect & Comp Engn Toronto ON Canada York Univ Dept Elect Engn & Comp Sci Toronto ON Canada James Cook Univ Coll Sci & Engn Townsville Australia Univ Windsor Dept Elect Engn & Comp Sci Windsor ON Canada

ISBN: (纸本)9798350381221;9798350381238

Recent advancements in image classification, object detection, and semantic segmentation have significantly accelerated medical image processing systems. However, for medical applications, these systems require exceptional performance to ensure reliability. high performance often entails increased computational load, power consumption, and system requirements. In this paper, we investigate the potential of an efficient implementation of very deep object detection neural networks on Phase Change Memory (PCM)-based memristive crossbar circuits for MRI image labeling, aiming to reduce inference time and power consumption. the neural network model is developed from scratch, incorporating the latest breakthroughs in object detection, and is able to achieve up to 84.2%mAP performance on a standard brain tumor dataset containing 3064 T1-weighted contrast-inhanced images from 233 patients with meningioma, glioma, and pituitary tumor. We also discuss the design decisions, challenges, model performance on different model configurations, and issues that need to be addressed in future work.

关键词： Memristor Phase Change Material (PCM) Medical Imaging Object Detection Deep Neural Network

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Enhanced Multi-Model Feature Fusion for Categorizing Diabetic Retinopathy Severity level 15

Enhanced Multi-Model Feature Fusion for Categorizing Diabeti...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Kanumuri, Deepika Lakshmi Ayinampudi, Gopika Kamma, Madhuri Anna, Somitha Suvarna, B. Department of Computer Science and Engineering AP Guntur India

ISBN: (纸本)9798350370249

Diabetic Retinopathy (DR), is a major concern for long-term diabetic individuals, leading to damage of eyesight if diagnosis is delayed. In this study, it aims to propose a multi-model fusion of two pre-trained models VGG19 and ResNet50, that leverages important details from the retinal fundus images. these are fused using a cross-pooling technique to capture significant image details, which are crucial for precise diagnosis. A neural network architecture is employed to classify DR severity levels with remarkable accuracy. this proposed model is experimented on the Kaggle APTOS 2019 dataset, which is a benchmark dataset. the results of Proposed model were impressive, achieving an accuracy of 83.33%. this high accuracy demonstrates the effectiveness of our methodology and shows the potential of fusion-based method in diagnosing *** proposed model offers a promising path for early intervention and preserving vision in diabetic patients and also demonstrates remarkable performance, especially in categorizing normal, mild, and moderate stages of retinal fundus images. © 2024 IEEE.

关键词： Cross pooling technique Diabetic Retinopathy Multi-model fusion Neural network Pretrained model

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Harnessing Cloud computing for Geophysical Exploration 35

Harnessing Cloud Computing for Geophysical Exploration

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35th IEEE International symposium on computer architecture and high performance computing (SBAC-PAD)

作者： Machado, Rodrigo C. Kunas, Cristiano A. Lorenzon, Arthur F. Carissimi, Alexandre Navaux, Philippe O. A. Univ Fed Rio Grande do Sul Inst Informat Porto Alegre RS Brazil

ISBN: (纸本)9798350381603

Geophysical exploration methods are important in discovering essential resources like oil and gas. However, traditional exploration often involves environmentally detrimental practices. To address this, software solutions simulate seismic imaging techniques for oil detection. In this scenario, the industry is now transitioning these applications to cloud-based Software as a Service (SaaS) models, offering benefits like resource optimization, eco-friendliness, and advanced data analytics. this shift, however, presents challenges in performance, scalability, and cost management. this paper presents a case study on the Fletcher modeling application as a SaaS in geophysical exploration, exploiting cloud hardware heterogeneity. through an extensive set of experiments on the Google Cloud instances with different multicore processors, we show that the Fletcher SaaS model scales with increased hardware resources, with AMD instances offering a better performance-cost trade-off than Intel.

关键词： Cloud computing performance Fletcher Geophysical Exploration

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Enhancing Oil Spill Detection Using Synthetic Aperture Radar With Dual Attention U-Net Model 15

Enhancing Oil Spill Detection Using Synthetic Aperture Radar...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Shanmukh, M. Phani Priya, S. Baghavathi Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350370249

Oil spills present a critical hazard in oceans, requiring advanced monitoring systems to rapidly detect and contain environmental damage. this work proposes a deep learning approach combining a UNet encoder-decoder architecture with a fully convolutional network in a dual attention model for accurate and efficient oil spill identification from SAR data. the model leverages the strengths of UNet and FCN for robust feature extraction and segmentation, while incorporating a dual attention mechanism to focus on the most salient spatial and channel-wise features. the dual attention model is compared against popular architectures like DeepLabV3, AlexNet, and PSPNet on public SAR datasets capturing oil spills. Results demonstrate the superior performance of the proposed dual attention model, achieving an impressive 97.5% accuracy in detecting oil spills outperforming all benchmark models evaluated. the high accuracy, coupled with the model's computational efficiency, highlights its potential for real-time autonomous oil spill monitoring and disaster management. the study underscores the efficacy of integrating attention mechanisms in deep learning models for SAR-based oil spill detection tasks. © 2024 IEEE.

关键词： Disaster prevention

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Optimizing Compiler for Quantum computing Using Qiskit Terra 15

Optimizing Compiler for Quantum Computing Using Qiskit Terra

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Balaji, Peddi Gowtham Chamikar, Peraboina Chowdary, Omkar Belwal, Meena Dept. of Computer Science and Engineering Amrita School of Computing Bangalore Amrita Vishwa Vidyapeetham India

ISBN: (纸本)9798350370249

the recent times of fast development of quantum computers has been followed by many researchers who want to run the larger and more difficult quantum algorithms on it and they have promised such speedup several times. Notwithstanding this, commercial quantum computers are currently fraught with instability and errors. this paper is centered on development of the customized compiler for quantum computing with aid of Qiskit Terra, a QIS framework. We target central classes such as circuit representation structures, programming techniques based on OpenQASM, challenges in the QC architecture, high-level routing optimization, and the structure of the Qiskit Terra compiler. In every circuit, the optimization is provided by methods of Qiskit Terra, including Trivial Layout, Noise Adaptive Layout, Dense Layout, and Custom Layout. these optimization levels address many problems that emerge when circuits are being compiled and executed, and thus, the optimization levels seek to boost circuit efficiency, reduce the count of gates, and enhance the overall performance. the introduction of numerous optimization passes starting from unroller passes that translate gates, the layout passes that map virtual to physical qubits, and the optimization algorithm aimed at the runtime reduction. Modern quantum compilation technologies are being currently developed based on economic (and theoretical) analyses to underpin the investigation and development of optimal quantum computation procedures. © 2024 IEEE.

关键词： Quantum computers

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ECSSD: Hardware/Data Layout Co-Designed In-Storage-computing architecture for Extreme Classification 23

ECSSD: Hardware/Data Layout Co-Designed In-Storage-Computing...

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50th Annual International symposium on computer architecture (ISCA)

作者： Li, Siqi Tu, Fengbin Liu, Liu Lin, Jilan Wang, Zheng Kang, Yangwook Ding, Yufei Xie, Yuan Univ Calif Santa Barbara Santa Barbara CA 93106 USA Hong Kong Univ Sci & Technol Hong Kong Peoples R China Rensselaer Polytech Inst Troy NY USA Samsung Semicond Inc San Jose CA USA Alibaba Grp DAMO Acad Sunnyvale CA USA

ISBN: (纸本)9798400700958

With the rapid growth of classification scale in deep learning systems, the final classification layer becomes extreme classification with a memory footprint exceeding the main memory capacity of the CPU or GPU. the emerging in-storage-computing technique offers an opportunity on account of the fact that SSD has enough storage capacity for the parameters of extreme classification. However, the limited performance of naive in-storage-computing schemes is insufficient to support the heavy workload of extreme classification. To this end, we propose ECSSD, the first hardware/data layout co-designed in-storage-computing architecture for extreme classification, based on the approximate screening algorithm. We propose an alignment-free floating-point MAC circuit technique to improve the computational ability under the limited area budget of in-storage-computing schemes so that the computational ability can match SSD's high internal bandwidth. We present a heterogeneous data layout design for the 4/32-bit weight data in the approximate screening algorithm to avoid data transfer interference and further utilize the internal DRAM bandwidth of SSD. Moreover, we propose a learning-based adaptive interleaving framework to balance the access workload in each flash channel and improve channel-level bandwidth utilization. Putting them together, our ECSSD achieves 3.24-49.87x performance improvements compared with state-of-the-art baselines.

关键词： In-storage-computing architecture Extreme classification

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GA-SFS: A Two-Stage Hybrid Algorithm for Feature Selection in Biomedical Data 15

GA-SFS: A Two-Stage Hybrid Algorithm for Feature Selection i...

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15th IEEE Annual computing and Communication Workshop and Conference, CCWC 2025

作者： Bales, Brian Davuluri, Monitha Hasan, Rashida California State University Department of Computer Science NorthridgeCA United States

ISBN: (纸本)9798331507695

In biomedical data analysis, feature selection is crucial, particularly for high-dimensional datasets where redundant or irrelevant features might affect model performance. Biomedical datasets introduce additional challenges, such as high dimensionality, small sample sizes, and complex data structures. Addressing these issues often requires hybrid, adaptive, or ensemble-based algorithms that can improve model robustness and interpretability. To meet these demands, this paper introduces GA-SFS, a two-stage hybrid algorithm that combines Genetic Algorithms (GA) with Sequential Forward Selection (SFS) to select informative features in complex biomedical datasets effectively. Experimental results on multiple datasets show that GA-SFS outperforms traditional feature selection methods in classification accuracy. © 2025 IEEE.

关键词： Genetic algorithms

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Facial Emotion Recognition Using computer Vision Techniques 15

Facial Emotion Recognition Using Computer Vision Techniques

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Gupta, Pradeep Kumar Varadharajan, Nayantara Ajith, Keerthana Singh, Tripty Patra, Payel Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India University of L'Aquila DISIM Department L'Aquila Italy

ISBN: (纸本)9798350370249

the ability to recognize facial emotions through computer vision has become a challenging yet crucial task in the field of image classification. this paper introduces a method for detecting facial emotions, focusing on the classification of seven distinct emotions which are neutral, sadness, anger, disgust, fear, happiness, and surprise. Using recent advancements in computer vision and machine learning, the proposed approach employs three distinct models: Convolutional Neural Network (CNN), ResNet50, and InceptionV3. Facial expression recognition poses unique challenges in image classification, and the adoption of deep learning has gained prominence for its effectiveness in solving this complex problem. the key challenge addressed in this research is the design of architectures that balance both simplicity and effectiveness. A simple architecture ensures quick training and easy implementation, while an effective one yields high accuracy on the test data. this research contributes to the ongoing efforts in advancing computer vision applications related to human emotions and highlights the potential for practical implementation in areas like affective computing and human computer interaction. the findings offer valuable insights into designing effective and efficient deep-learning architectures for facial emotion recognition with a multiclass approach. © 2024 IEEE.

关键词： Image classification

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