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A Data-Driven, Congestion-Aware and Open-Source Timing-Driven FPGA Placer Accelerated by GPUs 32

A Data-Driven, Congestion-Aware and Open-Source Timing-Drive...

引用

32nd IEEE Annual international symposium on Field-Programmable Custom computing Machines, FCCM 2024

作者： Xiong, Zhili Rajarathnam, Rachel Selina Pan, David Z. The University of Texas at Austin Department of Electrical & Computer Engineering TX United States

ISBN: (纸本)9798350372434

Placement plays a pivotal role in the modern FPGA physical design flow to determine the locations of the design instances among the available FPGA device resources, impacting routability and performance. Due to the lack of open-source accurate timing models for high-performance FPGAs, academic placement research has focused primarily on wirelength opti-mization rather than timing optimizations. This work presents an open-source timing-driven FPGA placer accelerated on GPU that employs a congestion-aware and data-driven timing model with timing optimizations at global placement and legalization. The placement objective incorporates an additional term to optimize the timing arcs in the lagrangian formulation. While packing and legalizing look-up tables (LUTs) and flip-flops (FFs), we emphasize timing-critical nets to remain within the Slice, minimizing overall path delay. On the ISPD'2016 contest benchmarks employing an AMD-Xilinx UltraScale architecture, our placer is 3× faster than the commercial AMD Vivado with similar critical path delay (×1.02) and 40% faster routing runtime. © 2024 IEEE.

关键词： Graphics processing unit

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AlexNet and Convolutional Neural Network Analysis in Thermogram Images for Automobile Application Using high performance computing

AlexNet and Convolutional Neural Network Analysis in Thermog...

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2024 international symposium on Networks, computers and Communications, ISNCC 2024

作者： Munian, Yuvaraj Alsmadi, Izzat Crumrine, Garrett Texas A&m University Dept. of Computational Engg. and Mathematical Science San AntonioTX United States

ISBN: (纸本)9798350364910

Lately, Deep learning and machine learning models have been vividly used in image classification to measure the accuracy of the proposed model. Deep learning networks always require high computing networks to increase the model's performance. computer vision plays a vital role in polishing the dataset by removing unwanted information, without the loss of important features. As per certain applications, two machine learning and deep learning algorithms with different system requirements are compared and summarized. In this paper, instead of the RGB image dataset, the thermogram image dataset has been considered for the process. The main concern in considering thermal images is due to the particular application. Here the thermal images have been collected during the nocturnal hours, especially on the dawn and dusk of the day. Convolutional neural networks are the machine learning models used in CPU and GPU-cluster configurations. AlexNet will be the deep learning model used here to compare with the CNN supervised learning algorithm. computer vision technique, HOG is used to reduce the complexity and intricacy of the dataset before getting into the network models. Because of the usage of deep network models' performance computing is expended to increase the speed of the whole process. Deer will be the spotted animal for most road accidents during the nocturnal hours in the United States. The results gaudily conclude the classification of machine learning and deep learning model detection for thermal and HOG images with the highest accuracy of 92% for the AlexNet. © 2024 IEEE.

关键词： Convolutional neural networks

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From Theory to Practice of Multidimensional Big Data Analytics over Big Healthcare Data: A Real-Life Case Study 28th

From Theory to Practice of Multidimensional Big Data Analyti...

引用

28th international symposium on Database Engineered Applications, IDEAS 2024

作者： Cuzzocrea, Alfredo Hafsaoui, Abderraouf Gallo, Carmine iDEA Lab University of Calabria Rende Italy Department of Computer Science University of Paris City Paris France

ISBN: (纸本)9783031834714

This paper provides practical and experimental results coming from the real-life research project that focuses on Big Data Analytics over Big Healthcare Data about chronical pain of patients in real-life hospitals and medical centers of North Italy (Region Lombardy), named as Pain-RELife. In this context, we devise innovative Multidimensional Big Data Analytics tools and a reference Cloud-based architecture that implements our analytics paradigm. Finally, we provide our analytical results based on advanced tools such as Multidimensional Clustering and Multidimensional Regression. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Advanced Big Data Systems Healthcare Data Science Multidimensional Big Data Analytics Tools

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Communication-Computation Overlapping for Parallel Multigrid Methods

Communication-Computation Overlapping for Parallel Multigrid...

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1st international Conference on Smart Energy Systems and Artificial Intelligence (SESAI)

作者： Nakajima, Kengo Univ Tokyo Informat Technol Ctr RIKEN R CCS Tokyo Japan

ISBN: (纸本)9798350364613;9798350364606

Preconditioned iterative methods based on the Krylov subspace technique are widely employed in various scientific and technical computing. When utilizing large-scale parallel computing systems, the communication overhead tends to increase with the growth in the number of nodes, making its reduction a crucial challenge. In parallel finite element methods (FEM) and finite volume methods (FVM), halo communication and computation overlapping (CC-Overlapping) are commonly employed, often in conjunction with the dynamic loop scheduling feature of OpenMP. This approach has been primarily applied to sparse matrix-vector products (SpMV) and explicit solvers. Previous studies by the author have proposed reordering techniques for applying CC-Overlapping to processes involving global data dependencies, such as the Conjugate Gradient method preconditioned by Incomplete Cholesky Factorization (ICCG). Successful implementations on massively parallel supercomputers demonstrated high parallel performance, but the application of CC-Overlapping was limited to SpMV. In the present work, the author proposes a method to apply CC-Overlapping to the forward and backward substitutions of the IC(0) smoother of the parallel Conjugate Gradient method preconditioned by Multigrid (MGCG). Using up to 4,096 nodes on Wisteria/EMEC-01 (Odyssey) with A64FX, performance improvement of approximately 40+% was achieved compared to the original implementation, while improvement was 20+% on 1,024 nodes of Oakbridge-CX system with Intel Xeon CPU's.

关键词： Parallel computing Iterative Solvers Multigrid Manycore Communication-Computation Overlapping

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EXION: Exploiting Inter-and Intra-Iteration Output Sparsity for Diffusion Models 31

EXION: Exploiting Inter-and Intra-Iteration Output Sparsity ...

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31st IEEE international symposium on high performance computer architecture, HPCA 2025

作者： Heo, Jaehoon Putra, Adiwena Yoon, Jieon Yune, Sungwoong Lee, Hangyeol Kim, Ji-Hoon Kim, Joo-Young KAIST Daejeon Korea Republic of

ISBN: (纸本)9798331506476

Over the past few years, diffusion models have emerged as novel solutions in AI industries, offering the capability to generate diverse, multi-modal outputs such as images, videos, and motions from input prompts (i.e., text). Despite their impressive capabilities, diffusion models face significant challenges in computing, including excessive latency and energy consumption due to the numerous iterations in model architecture. Although prior works specialized in transformer acceleration can be applied to diffusion models, given that transformers are key components, the problem of the iterative nature of diffusion models still needs to be *** this paper, we present EXION, the first software-hardware co-designed diffusion accelerator that solves the computation challenges of excessive iterations by exploiting the unique inter-and intra-iteration output sparsity in diffusion models. To this end, we propose two software-level optimizations in EXION. First, we introduce the FFN-Reuse algorithm that identifies and skips redundant computations in FFN layers across different iterations (i.e., inter-iteration sparsity). Second, we use a modified eager prediction method that employs two-step leading-one detection to accurately predict the attention score in diffusion models, skipping unnecessary computations within an iteration (i.e., intra-iteration sparsity). We also introduce a novel data compaction mechanism named ConMerge, which can enhance hardware utilization by condensing and merging large and sparse matrices into small and compact forms. Finally, EXION has a dedicated hardware architecture that supports the above sparsity-inducing algorithms, translating high output sparsity into improved energy efficiency and performance. To verify the feasibility of the EXION accelerator, we first demonstrate that it has no impact on accuracy in various types of multi-modal diffusion models, including text-to-motion, -audio, -image, and -video. We then instantiate EXION in b

关键词： computer software reusability

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Real-Time Evaluation of Aircraft Instruments 19th

Real-Time Evaluation of Aircraft Instruments

引用

19th international symposium on Visual computing

作者： Munson, Matthew Olson, Clark F. Univ Washington Bothell WA 98011 USA

ISBN: (纸本)9783031773884;9783031773891

While digital displays and glass cockpits have become widespread in modern aircraft, analog instruments remain. These gauges can be challenging to digitize or integrate into automated safety systems. This work investigates the application of computer vision to evaluate aircraft instruments of varying complexity. For ease of acquisition, training data was recorded from a flight simulator and used to train neural networks. The resulting models have high accuracy when evaluating single pointer gauges in lighting conditions similar to the training data set, as well as with entirely different lighting conditions. performance remains robust even with more complex instruments, such as dual pointer airspeed gauges and attitude indicators. Potential future work on this system includes applying it to real-life aircraft and integration with safety systems, including detection of instrument display failures.

关键词： Aircraft Instruments computer Vision Deep Learning

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An Experimental Comparison of RISC-V Processors: performance, Power, Area and Security - Special Session Paper 37

An Experimental Comparison of RISC-V Processors: Performance...

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37th IEEE international symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems, DFT 2024

作者： Lazzeri, Elia Forlin, Bruno Endres Furano, Gianluca Ottavi, Marco Cassano, Luca Politecnico di Milano Dipartimento di Elettronica Informazione e Bioingegneria Italy University of Twente Faculty of Electrical Engineering Mathematics and Computer Science Netherlands Europeran Space Agency European Space Research and Technology Centre Netherlands

ISBN: (纸本)9798350366884

The RISC-V instruction set architecture (ISA) has garnered significant interest from both industry and academia because of its open source nature. Recent years have witnessed a surge in published implementations of the RISC-V ISA, with various companies actively pursuing its adoption in their products. However, a comprehensive analysis comparing these emerging RISC-V processors with each other and against established embedded computing platforms has not yet been published. This paper presents an experimental evaluation of three high-performance RISC-V processors: BOOM, NOEL-V, and CVA6 (formerly Ariane). The investigation is conducted on a Xilinx Kintex-7 Field-Programmable Gate Array (FPGA) platform. We perform a detailed analysis of critical performance metrics, covering area footprint, power consumption, and performance efficiency. Additionally, we assess the security posture of these cores against transient execution attacks, a prominent contemporary security threat. Finally, a comparative evaluation is undertaken between the RISC-V processors and two conventional application-level ARM processors to elucidate technological discrepancies and application suitability. This analysis aims to provide valuable insights into the current state and potential of RISC-V processors within the embedded computing domain. © 2024 IEEE.

关键词： ARM processors

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Convolution Operators for Deep Learning Inference on the Fujitsu A64FX Processor 34

Convolution Operators for Deep Learning Inference on the Fuj...

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34th IEEE international symposium on computer architecture and high performance computing (SBAC-PAD)

作者： Dolz, Manuel F. Martinez, Hector Alonso, Pedro Quintana-Orti, Enrique S. Univ Jaume I Castellon Castellon de La Plana Spain Univ Cordoba Cordoba Spain Univ Politecn Valencia Valencia Spain

ISBN: (数字)9781665451550

ISBN: (纸本)9781665451550

The convolution operator is a crucial kernel for many computer vision and signal processing applications that rely on deep learning (DL) technologies. As such, the efficient implementation of this operator has received considerable attention in the past few years for a fair range of processor architectures. In this paper, we follow the technology trend toward integrating long SIMD (single instruction, multiple data) arithmetic units into high performance multicore processors to analyse the benefits of this type of hardware acceleration for latency-constrained DL workloads. For this purpose, we implement and optimise for the Fujitsu processor A64FX, three distinct methods for the calculation of the convolution, namely, the lowering approach, a blocked variant of the direct convolution algorithm, and the Winograd minimal filtering algorithm. Our experimental results include an extensive evaluation of the parallel scalability of these three methods and a comparison of their global performance using three popular DL models and a representative dataset.

关键词： Convolutional neural networks high performance SIMD arithmetic units ARM-based A64FX processor

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DynamoLLM: Designing LLM Inference Clusters for performance and Energy Efficiency 31

DynamoLLM: Designing LLM Inference Clusters for Performance ...

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31st IEEE international symposium on high performance computer architecture, HPCA 2025

作者： Stojkovic, Jovan Zhang, Chaojie Goiri, Inigo Torrellas, Josép Choukse, Esha University of Illinois at Urbana-Champaign United States Microsoft Azure Research - Systems United States

ISBN: (纸本)9798331506476

The rapid evolution and widespread adoption of generative large language models (LLMs) have made them a pivotal workload in various applications. Today, LLM inference clusters receive a large number of queries with strict Service Level Objectives (SLOs). To achieve the desired performance, these models execute on power-hungry GPUs, causing inference clusters to consume large amounts of energy and, consequently, result in substantial carbon emissions. Fortunately, we find that there is an opportunity to improve energy efficiency by exploiting the heterogeneity in inference compute properties and the fluctuations in inference workloads. However, the diversity and dynamicity of these environments create a large search space, where different system configurations (e.g., number of instances, model parallelism, and GPU frequency) translate into different energy-performance trade-offs. To address these challenges, we propose DynamoLLM, the first energy-management framework for LLM inference environments. DynamoLLM automatically and dynamically reconfigures the inference cluster to optimize for energy of LLM serving under the services' performance SLOs. We show that at a service level, on average, DynamoLLM conserves 52% of the energy and 38% of the operational carbon emissions, and reduces the cost to the customer by 61%, while meeting the latency SLOs. © 2025 IEEE.

关键词： computer graphics equipment

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Evaluating Kubernetes at the Edge for Fault Tolerant Multi-Camera computer Vision Applications 23

Evaluating Kubernetes at the Edge for Fault Tolerant Multi-C...

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23rd IEEE/ACM international symposium on Cluster, Cloud and Internet computing (CCGrid)

作者： Heckmann, Owen Ravindran, Arun Univ N Carolina Dept Elect & Comp Engn Charlotte NC 28223 USA

ISBN: (纸本)9798350302080

The rise of AI-powered computer vision algorithms offers the possibility of visual sensing of the environment in IoT applications through the widespread use of low-cost video cameras. The need for low latency, bandwidth limitations, and privacy concerns associated with video data motivates the use of edge computing for computer vision applications. However, unlike cloud computing with almost unbounded resources, the edge is characterized by compute nodes of limited capacity and power budget. Additionally, fault tolerance is limited due to replication costs at the edge. In this poster, we present our initial work on evaluating the performance of an edge-specific version of Kubernetes on a Raspberry Pi4 cluster for multi-camera computer vision applications. Kubernetes enables automated deployment and management of containerized distributed applications to run at scale across a cluster of compute and storage nodes, while handling node failures. However, existing literature has not characterized the resource consumption and latency impact of Kubernetes for computer vision applications on realistic edge clusters. Our experimental results indicate that while Kubernetes can deliver fault tolerance at the edge, the choices made in the design of containers pods significantly affects the observed tail latency on a low power edge cluster.

关键词： Kubernetes edge multi-camera computer vision

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