Relevant bibliographies by topics / Heterogeneous acceleration

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Heterogeneous acceleration'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Heterogeneous acceleration"

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Liu, Xing, Ziheng Dai, Qi Wang, and Zhanpeng Li. "Computing Acceleration of Medical Image Processing Based on Multi-Accelerator Heterogeneous Systems." ACM SIGAPP Applied Computing Review 25, no. 1 (2025): 16–24. https://doi.org/10.1145/3727257.3727259.

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Medical image processing (MIP) can make the medical diagnosis more accurate and efficient. However, the operating process of MIP involves many computationally-intensive tasks, which results in a long computational time for MIP. To optimize the processing time of MIP, many research works use the heterogeneous computing architecture (HCA) to accelerate MIP. These works are effective. However, most of them focus on single-accelerator HCA (SA-HCA), which commonly consists of one CPU plus one kind of accelerators, but pay less attention to the multi-accelerator HCA (MA-HCA) such as CPU/FPGA/NPU or
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Chun F. Ong and M. N. Marsono. "Heterogeneous Hardware Architecture with Linear Algebra Acceleration." ELEKTRIKA- Journal of Electrical Engineering 24, no. 1 (2025): 53–56. https://doi.org/10.11113/elektrika.v24n1.633.

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Linear algebra is essential in machine learning for dealing with large datasets. Linear algebra acceleration is directly related to the hardware used. Many works have proposed linear algebra accelerator architectures with the goal of improving energy efficiency and speed. The characterization of trade-offs in balancing acceleration and programmability of software routines is still insufficiently explored, particularly for edge analytics. Therefore, this paper proposes a heterogeneous hardware architecture consisting of a RISC-V system-on-chip and a linear algebra accelerator. Tested on Efinix
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Wei, Xian Min. "Acceleration Components in Heterogeneous Supercomputers." Advanced Materials Research 204-210 (February 2011): 765–68. http://dx.doi.org/10.4028/www.scientific.net/amr.204-210.765.

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General-purpose processors continue to improve computing performance, but its energy consumption has far exceeded the increase in the proportion of performance. Specifically designed for the special purpose processor in the case of relatively low power consumption, can provide better performance. The general-purpose processor as the main control unit, special purpose processors as accelerators consisting of heterogeneous supercomputers will become a trend in supercomputer development. This paper introduces and analyzes several heterogeneous supercomputers used to build the acceleration compone
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Rodriguez-Borbon, Jose M., Xiaoyin Ma, Amit K. Roy-Chowdhury, and Walid A. Najjar. "Heterogeneous Acceleration of HAR Applications." IEEE Transactions on Circuits and Systems for Video Technology 30, no. 3 (2020): 888–902. http://dx.doi.org/10.1109/tcsvt.2019.2895304.

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Gou, Xiaopeng, Qi Ge, Quan Guo, et al. "Hardware-Accelerated Data Readout Platform Using Heterogeneous Computing for DNA Data Storage." Applied Sciences 15, no. 9 (2025): 5050. https://doi.org/10.3390/app15095050.

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DNA data storage has emerged as a promising alternative to traditional storage media due to its high density and durability. However, large-scale DNA storage systems generate massive sequencing reads, posing substantial computational complexity and latency challenges for data readout. Here, we propose a novel heterogeneous computing architecture based on a field-programmable gate array (FPGA) to accelerate DNA data readout. The software component, running on a general computing platform, manages data distribution and schedules acceleration kernels. Meanwhile, the hardware acceleration kernel i
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LIU, SHAOSHAN, WON W. RO, CHEN LIU, et al. "INTRODUCING THE EXTREMELY HETEROGENEOUS ARCHITECTURE." Journal of Interconnection Networks 13, no. 03n04 (2012): 1250010. http://dx.doi.org/10.1142/s0219265912500107.

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The computer industry is moving towards two extremes: extremely high-performance high-throughput cloud computing, and low-power mobile computing. Cloud computing, while providing high performance, is very costly. Google and Microsoft Bing spend billions of dollars each year to maintain their server farms, mainly due to the high power bills. On the other hand, mobile computing is under a very tight energy budget, but yet the end users demand ever increasing performance on these devices. This trend indicates that conventional architectures are not able to deliver high-performance and low power c
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Riebler, Heinrich, Gavin Vaz, Tobias Kenter, and Christian Plessl. "Automated code acceleration targeting heterogeneous openCL devices." ACM SIGPLAN Notices 53, no. 1 (2018): 417–18. http://dx.doi.org/10.1145/3200691.3178534.

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Cao, Kai, Qizhong Wu, Lingling Wang, et al. "GPU-HADVPPM4HIP V1.0: using the heterogeneous-compute interface for portability (HIP) to speed up the piecewise parabolic method in the CAMx (v6.10) air quality model on China's domestic GPU-like accelerator." Geoscientific Model Development 17, no. 17 (2024): 6887–901. http://dx.doi.org/10.5194/gmd-17-6887-2024.

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Abstract. Graphics processing units (GPUs) are becoming a compelling acceleration strategy for geoscience numerical models due to their powerful computing performance. In this study, AMD's heterogeneous-compute interface for portability (HIP) was implemented to port the GPU acceleration version of the piecewise parabolic method (PPM) solver (GPU-HADVPPM) from NVIDIA GPUs to China's domestic GPU-like accelerators like GPU-HADVPPM4HIP. Further, it introduced the multi-level hybrid parallelism scheme to improve the total computational performance of the HIP version of the CAMx (Comprehensive Air
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Meng, Yuan, and Jun Yang. "Research on heterogeneous acceleration platform based on FPGA." ITM Web of Conferences 45 (2022): 01029. http://dx.doi.org/10.1051/itmconf/20224501029.

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In the context of today’s artificial intelligence, the volume of data is exploding. Although scaling distributed clusters horizontally to cope with the increasing demands on computing power for massive data processing is feasible. But the unlimited addition of nodes will lead to bloated cluster size. Most of the transistors in CPUs are used to build cache memory and control units, which are not efficient for computing operations of massive data processing. Currently, academia uses hardware devices such as GPU (Graphics Processing Unit), ASIC (Application Specific Integrated Circuit), FPGA (Fie
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Cornelis, Jan G., Jan Lemeire, Tim Bruylants, and Peter Schelkens. "Heterogeneous acceleration of volumetric JPEG 2000 using OpenCL." International Journal of High Performance Computing Applications 31, no. 3 (2016): 229–45. http://dx.doi.org/10.1177/1094342016646438.

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Dissertations / Theses on the topic "Heterogeneous acceleration"

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Conti, Francesco <1988&gt. "Heterogeneous Architectures For Parallel Acceleration." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2016. http://amsdottorato.unibo.it/7406/1/phd_thesis_AMS_wFRONTESPIZIO.pdf.

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To enable a new generation of digital computing applications, the greatest challenge is to provide a better level of energy efficiency (intended as the performance that a system can provide within a certain power budget) without giving up a systems's flexibility. This constraint applies to digital system across all scales, starting from ultra-low power implanted devices up to datacenters for high-performance computing and for the "cloud". In this thesis, we show that architectural heterogeneity is the key to provide this efficiency and to respond to many of the challenges of tomorrow's compu
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Conti, Francesco <1988&gt. "Heterogeneous Architectures For Parallel Acceleration." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2016. http://amsdottorato.unibo.it/7406/.

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To enable a new generation of digital computing applications, the greatest challenge is to provide a better level of energy efficiency (intended as the performance that a system can provide within a certain power budget) without giving up a systems's flexibility. This constraint applies to digital system across all scales, starting from ultra-low power implanted devices up to datacenters for high-performance computing and for the "cloud". In this thesis, we show that architectural heterogeneity is the key to provide this efficiency and to respond to many of the challenges of tomorrow's compu
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Somers, Gregory W. "Acceleration of Block-Aware Matrix Factorization on Heterogeneous Platforms." Thesis, Université d'Ottawa / University of Ottawa, 2016. http://hdl.handle.net/10393/35128.

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Block-structured matrices arise in several contexts in circuit simulation problems. These matrices typically inherit the pattern of sparsity from the circuit connectivity. However, they are also characterized by dense spots or blocks. Direct factorization of those matrices has emerged as an attractive approach if the host memory is sufficiently large to store the block-structured matrix. The approach proposed in this thesis aims to accelerate the direct factorization of general block-structured matrices by leveraging the power of multiple OpenCL accelerators such as Graphical Processing Un
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Ferhatović, Nihad. "Analyzing the latency overheads of acceleration in heterogeneous systems." Thesis, Mälardalens högskola, Inbyggda system, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-55985.

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Importance of low-latency heterogeneous systems in today’s world is immeasurable, which was proven in the last few years especially with the appearance of pandemic Covid-19 by forcing us to use online streaming technologies. By tackling more complex tasks and problems, algorithms require computational power that often exceeds the capabilities of pieces of technology used for computational tasks. In order to solve this problem, different methods are used. Hardware acceleration as a type of process acceleration is one of the most used methods in the field of AI mostly because it easily out-perfo
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Pagani, Marco. "Enabling Predictable Hardware Acceleration in Heterogeneous SoC-FPGA Computing Platforms." Thesis, Lille 1, 2020. http://www.theses.fr/2020LIL1I016.

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Les architectures informatiques modernes pour les systèmes intégrés évoluent vers des plateformes de plus en plus hétérogènes, comprenant différents types de processeurs et d'accélérateurs. Cette évolution est entraînée par la nécessité de répondre à la demande croissante de capacité de calcul par les systèmes cyber-physiques modernes. Ces systèmes doivent acquérir et traiter de grandes quantités de données, provenant de différents capteurs, afin d'exécuter les tâches de contrôle et de surveillance nécessaires. Ces exigences se traduisent par la nécessité d'exécuter des charges de calcul compl
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Xue, Weicheng. "CPU/GPU Code Acceleration on Heterogeneous Systems and Code Verification for CFD Applications." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/102073.

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Computational Fluid Dynamics (CFD) applications usually involve intensive computations, which can be accelerated through using open accelerators, especially GPUs due to their common use in the scientific computing community. In addition to code acceleration, it is important to ensure that the code and algorithm are implemented numerically correctly, which is called code verification. This dissertation focuses on accelerating research CFD codes on multi-CPUs/GPUs using MPI and OpenACC, as well as the code verification for turbulence model implementation using the method of manufactured solution
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Schultek, Brian Robert. "Design and Implementation of the Heterogeneous Computing Device Management Architecture." University of Dayton / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1417801414.

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Bijanapalli, Chakri Ramakrishna. "Enabling the use of Heterogeneous Computing for Bioinformatics." Thesis, Virginia Tech, 2013. http://hdl.handle.net/10919/23866.

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The huge amount of information in the encoded sequence of DNA and increasing interest in uncovering new discoveries has spurred interest in accelerating the DNA sequencing and alignment processes. The use of heterogeneous systems, that use different types of computational units, has seen a new light in high performance computing in recent years; However expertise in multiple domains and skills required to program these systems is causing an hindrance to bioinformaticians in rapidly deploying their applications into these heterogeneous systems. This work attempts to make an heterogeneous system
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Chen, Chong. "Acceleration of Computer Based Simulation, Image Processing, and Data Analysis Using Computer Clusters with Heterogeneous Accelerators." University of Dayton / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=dayton148036732102682.

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Winter, Frank [Verfasser], and Andreas [Akademischer Betreuer] Schaefer. "Investigation of hadron matter using lattice QCD and implementation of lattice QCD applications on heterogeneous multicore acceleration processors / Frank Winter. Betreuer: Andreas Schaefer." Regensburg : Universitätsbibliothek Regensburg, 2011. http://d-nb.info/1023361698/34.

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Books on the topic "Heterogeneous acceleration"

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Jain, Vikram. Towards Heterogeneous Multi-Core System-on-Chips for Edge Machine Learning: Journey from Single-Core Acceleration to Multi-core Heterogeneous Systems. Springer, 2023.

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Winter, Frank. Investigation of Hadron Matter Using Lattice QCD and Implementation of Lattice QCD Applications on Heterogeneous Multicore Acceleration Processors. Universitatsverlag Regensburg, 2011.

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Dean, Michael, and Karobi Moitra. Biology of Neoplasia. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190238667.003.0002.

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The term “cancer” encompasses a large heterogeneous group of diseases that involve uncontrolled cell growth, division, and survival, culminating in local invasion and/or distant metastases. Cancer is fundamentally a genetic disease at the cellular level. Tumors occur because clones of abnormal cells acquire multiple lesions in DNA, nearly always involving mutations, chromosomal rearrangements, and extensive alteration of the epigenome. Up to 10% of cancers also involve inherited germline mutations that are moderately to highly penetrant. Cancers begin as localized growths or premalignant lesio
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Book chapters on the topic "Heterogeneous acceleration"

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Niu, Xinyu, Tim Todman, and Wayne Luk. "Self-adaptive Hardware Acceleration on a Heterogeneous Cluster." In Natural Computing Series. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39675-0_9.

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de Haan, Redmer, Tom van der Sande, and Erjen Lefeber. "Prediction Based Cooperative Adaptive Cruise Control for Heterogeneous Platoons with Delays." In Lecture Notes in Mechanical Engineering. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-70392-8_31.

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AbstractWe present a prediction based controller for heterogeneous platoons with actuation delay. By using a prediction of the ego vehicle’s acceleration and compensating the ego vehicle’s influence on the error dynamics, we obtain a controller that achieves input-to-state stability (ISS) with respect to the preceding vehicle’s acceleration. The result is a controller that does not require driveline information of the preceding vehicle, which enables platooning in a heterogeneous setting. An analysis is presented of the string stability properties of the system with both actuation and communic
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Jain, Vikram, and Marian Verhelst. "Energy-Efficient Single-Core Hardware Acceleration." In Towards Heterogeneous Multi-core Systems-on-Chip for Edge Machine Learning. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-38230-7_5.

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Rivera-Alvarado, Ernesto, and Francisco J. Torres-Rojas. "Bounding Volume Hierarchy Acceleration Through Tightly Coupled Heterogeneous Computing." In Communications in Computer and Information Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-41005-6_7.

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Boudjouk, Philip. "Acceleration of Synthetically Useful Heterogeneous Reactions Using Ultrasonic Waves." In ACS Symposium Series. American Chemical Society, 1987. http://dx.doi.org/10.1021/bk-1987-0333.ch013.

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Kurek, Maciej, Tobias Becker, Ce Guo, et al. "Self-aware Hardware Acceleration of Financial Applications on a Heterogeneous Cluster." In Natural Computing Series. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39675-0_12.

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Jain, Vikram, and Marian Verhelst. "Networks-on-Chip to Enable Large-Scale Multi-core ML Acceleration." In Towards Heterogeneous Multi-core Systems-on-Chip for Edge Machine Learning. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-38230-7_8.

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Zhai, X. M. Shine, David Gutzwiller, Kunal Puri, and Charles Hirsch. "GPU Acceleration of the FINE/FR CFD Solver in a Heterogeneous Environment with OpenACC Directives." In Accelerator Programming Using Directives. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74224-9_3.

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Suarez, Jaime, Andres L. Marin, Dimitrios Komnos, Alessandro Tansini, and Georgios Fontaras. "Driving Style Characterisation and its Impact on Vehicle Energy Efficiency." In Lecture Notes in Mobility. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-85578-8_16.

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Abstract The driving style is a crucial variable for real-world vehicle energy performance. This study uses a state-of-the-art methodology to separately assess its impact on the energy consumption. A benchmark driving cycle is modified according to specific driving styles, and the corresponding energy consumption is theoretically simulated. The driving style characterisation includes acceleration, braking and gear-shifting patterns for each driver through ad-hoc metrics. The methodology establishes a comparative analysis regarding the energy impact of a pool of drivers. Furthermore, the variab
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Dutta, Hritam, Frank Hannig, and Jürgen Teich. "Performance Matching of Hardware Acceleration Engines for Heterogeneous MPSoC Using Modular Performance Analysis." In Architecture of Computing Systems – ARCS 2009. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00454-4_23.

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Conference papers on the topic "Heterogeneous acceleration"

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Gao, Xiang, Yuting Wu, Rongkai Liu, and Xiancheng Lin. "UFHDA: A Unified Framework for Heterogeneous Big Data Acceleration." In 2024 IEEE 7th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). IEEE, 2024. http://dx.doi.org/10.1109/itnec60942.2024.10733108.

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Xue, Zun, Yuchen Liu, Yuyang Ye, Tinghuan Chen, Hao Yan, and Longxing Shi. "Aging-Aware Logic Restructure Acceleration Based on Heterogeneous Graph Learning." In 2024 2nd International Symposium of Electronics Design Automation (ISEDA). IEEE, 2024. http://dx.doi.org/10.1109/iseda62518.2024.10617843.

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Wiggins, Samuel, Yuan Meng, Mahesh A. Iyer, and Viktor Prasanna. "A Heterogeneous Acceleration System for Attention-Based Multi-Agent Reinforcement Learning." In 2024 34th International Conference on Field-Programmable Logic and Applications (FPL). IEEE, 2024. http://dx.doi.org/10.1109/fpl64840.2024.00040.

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Jiang, Kai, Xiongru Wang, Shuai Wang, Haijun Xue, Fan Yu, and Rui Li. "A Generalized Design for Database Heterogeneous Query Acceleration Based on FPGA." In 2024 China Automation Congress (CAC). IEEE, 2024. https://doi.org/10.1109/cac63892.2024.10864689.

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Grailoo, Mahdieh, Tooraj Nikoubin, and Jose Nunez-Yanez. "Hardware Acceleration of Molecular Property Graph Prediction on a Heterogeneous Edge Platform." In 2024 IEEE Nordic Circuits and Systems Conference (NorCAS). IEEE, 2024. http://dx.doi.org/10.1109/norcas64408.2024.10752459.

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Liu, Yuchen, Chengyu Wang, Zhengrui Shi, et al. "Dynamic Acceleration-Aware Risk Potential Field Car-Following Model for Heterogeneous Individuals." In 2025 IEEE Wireless Communications and Networking Conference (WCNC). IEEE, 2025. https://doi.org/10.1109/wcnc61545.2025.10978316.

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Dodson, Zackary, Brendan Kochunas, and Edward Larsen. "Fourier Analysis of Coarse Mesh Finite Difference and Linear Diffusion Acceleration for Spatially Heterogeneous Systems." In Mathematics and Computation 2021. American Nuclear Society, 2021. https://doi.org/10.13182/xyz-33868.

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Wang, Letian, Pinfeng Jiang, Zichong Zhang, et al. "A Heterogeneous FTJ-Based Computing-in-Memory Architecture for Vision Transformer Acceleration via Hardware and Algorithm Co-Design." In 2025 9th IEEE Electron Devices Technology & Manufacturing Conference (EDTM). IEEE, 2025. https://doi.org/10.1109/edtm61175.2025.11040746.

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Pan, Yueyuan, Tianxu Zhang, and Haowen Ding. "Research on real-time infrared image matching acceleration technology based on dual multi-core DSP+FPGA heterogeneous system." In Fourth International Conference on Electronics Technology and Artificial Intelligence (ETAI 2025), edited by Shaohua Luo and Akash Saxena. SPIE, 2025. https://doi.org/10.1117/12.3068556.

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Rubenstein, Greg, Ananth Sridharan, David Moy, and Inderjit Chopra. "A Python-based Framework for Real-time Simulation using Comprehensive Analysis." In Vertical Flight Society 72nd Annual Forum & Technology Display. The Vertical Flight Society, 2016. http://dx.doi.org/10.4050/f-0072-2016-11505.

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This paper describes an open-source framework to use a rotorcraft comprehensive analysis with elastic blades and free wake for real-time simulation. Several acceleration strategies are combined with load-balanced parallelization algorithms to achieve this goal. Up to 24 times speed-up for trim, and 33 times speed-up for time marching were demonstrated for a single rotor system with 4 blades, allowing for 5 deg azimuthal time steps. Heterogeneous computing for accelerated analysis with free wake was also explored as a preliminary step towards real-time wake modeling. 23 times/29 times speed-ups
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Reports on the topic "Heterogeneous acceleration"

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Bonilla-Mejía, Leonardo, Luz Adriana Flórez, Didier Hermida, et al. Is the Covid-19 Pandemic Fast-Tracking Automation in Developing Countries? Evidence from Colombia. Banco de la República, 2022. http://dx.doi.org/10.32468/be.1209.

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This paper assesses whether the Covid-19 pandemic accelerated automation in developing countries. We studied the case of Colombia, a country with low R&amp;D and productivity and with high labor informality and unemployment. We estimated event-study models to assess the differential effect of the pandemic on job openings and salaried employment by the potential degree of automation of each occupation. Our results suggest that both vacancies and salaried employment fell more in highly automatable occupations during the pandemic and have since experienced a slower recovery. The effect of the pan
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