FPGAs versus GPUs in Data centers

Falsafi, Babak; Dally, Bill; Singh, Desh Deepak; Chiou, Derek; Yi, Joshua J.; Sendag, Resit

doi:10.1109/mm.2017.19

Cited by 30 publications

(12 citation statements)

References 1 publication

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“…Mature GPU programming frameworks such as CUDA, OpenCL and OpenACC enable relatively simple programming compared to other accelerators such as FPGAs [44]. Libraries such as Thrust [17] and CUBLAS [113] supply the programmer with high-performance implementations for common tasks such as parallel reduction, sorting, and linear algebra operations.…”

Section: Graphics Processing Units (Gpus)mentioning

confidence: 99%

A Survey on Agent-based Simulation Using Hardware Accelerators

et al. 2019

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Due to decelerating gains in single-core CPU performance, computationally expensive simulations are increasingly executed on highly parallel hardware platforms. Agent-based simulations, where simulated entities act with a certain degree of autonomy, frequently provide ample opportunities for parallelisation. Thus, a vast variety of approaches proposed in the literature demonstrated considerable performance gains using hardware platforms such as many-core CPUs and GPUs, merged CPU-GPU chips as well as FPGAs. Typically, a combination of techniques is required to achieve high performance for a given simulation model, putting substantial burden on modellers. To the best of our knowledge, no systematic overview of techniques for agent-based simulations on hardware accelerators has been given in the literature. To close this gap, we provide an overview and categorization of the literature according to the applied techniques. Since at the current state of research, challenges such as the partitioning of a model for execution on heterogeneous hardware are still a largely manual process, we sketch directions for future research towards automating the hardware mapping and execution. This survey targets modellers seeking an overview of suitable hardware platforms and execution techniques for a specific simulation model, as well as methodology researchers interested in potential research gaps requiring further exploration.

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Section: Graphics Processing Units (Gpus)mentioning

confidence: 99%

A Survey on Agent-based Simulation Using Hardware Accelerators

et al. 2019

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“…F5-HD aims to abstract away the complexities behind employing FPGAs for accelerating AI applications [27]. F5-HD is an automated framework that generates synthesizable FPGA-based HD implementation in Verilog, considering the user-specified criteria, e.g., power budget, performance-accuracy trade-off, and FPGA model (available resources).…”

Section: F5-hd Framework Overviewmentioning

confidence: 99%

F5-Hd

Salamat

Imani

Khaleghi

et al. 2019

Proceedings of the 2019 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays

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Hyperdimensional (HD) computing is a novel computational paradigm that emulates the brain functionality in performing cognitive tasks. The underlying computation of HD involves a substantial number of element-wise operations (e.g., addition and multiplications) on ultra-wise hypervectors, in the granularities of as small as a single bit, which can be effectively parallelized and pipelined. In addition, though different HD applications might vary in terms of number of input features and output classes (labels), they generally follow the same computation flow. Such characteristics of HD computing inimitably matches with the intrinsic capabilities of FPGAs, making these devices a unique solution for accelerating these applications. In this paper, we propose F5-HD, a fast and flexible FPGA-based framework for refreshing the performance of HD computing. F5-HD eliminates the arduous task of handcrafted designing of hardware accelerators by automatically generating an FPGA implementation of HD accelerator leveraging a template of optimized processing elements, according to the applications specification and user's constraint. Our evaluations using different classification benchmarks revealed that F5-HD provides 86.9× and 7.8× (11.9× and 1.7×) higher energy efficiency improvement and faster training (inference) as compared to an optimized implementation of HD on AMD R9 390 GPU, respectively. CCS CONCEPTS • Hardware → Reconfigurable logic and FPGAs; Electronic design automation; • Computing methodologies → Machine learning approaches.

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“…At the same time, the success of DNNs more generally has motivated the research and development of many specialized system architectures and accelerators both in academia and in industry. An excellent overview of the challenges of accelerating DNNs in hardware and a comprehensive survey of many techniques and frameworks that have been proposed so far in the literature is provided in [10]. In terms of implementation, DNN frameworks mainly target CPUs and GPUs.…”

Section: Introductionmentioning

confidence: 99%

Accelerating Deep Neural Networks for Real-time Data Selection for High-resolution Imaging Particle Detectors

Jwa

Guglielmo

Carloni

et al. 2019

2019 New York Scientific Data Summit (NYSDS)

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This paper presents the custom implementation, optimization, and performance evaluation of convolutional neural networks on field programmable gate arrays, for the purposes of accelerating deep neural network inference on large, twodimensional image inputs. The targeted application is that of data selection for high-resolution particle imaging detectors, and in particular liquid argon time projection chamber detectors, such as that employed by the future Deep Underground Neutrino Experiment. We motivate this particular application based on the excellent performance of deep neural networks on classifying simulated raw data from the DUNE LArTPC, combined with the need for power-efficient data processing in the case of remote, long-term, and limited-access operating detector conditions.

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FPGAs versus GPUs in Data centers

Cited by 30 publications

References 1 publication

A Survey on Agent-based Simulation Using Hardware Accelerators

A Survey on Agent-based Simulation Using Hardware Accelerators

F5-Hd

Accelerating Deep Neural Networks for Real-time Data Selection for High-resolution Imaging Particle Detectors

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