Evaluating Spatial Accelerator Architectures with Tiled Matrix-Matrix Multiplication

Moon, Gordon Euhyun; Kwon, Hyoukjun; Jeong, Geonhwa; Chatarasi, Prasanth; Rajamanickam, Sivasankaran; Krishna, Tushar

doi:10.1109/tpds.2021.3104240

Cited by 20 publications

(2 citation statements)

References 31 publications

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“…However, typical NN layouts used for benchmarking purposes, such as ResNet152 and AlexNet, 39 require a total number of 25 and 62 million trainable parameters, respectively, that can hardly fit as hardware-coded information even into the available number of computational elements supported by current top-class GPU and TPU platforms. This has turned tiled matrix multiplication (TMM) into the mainstream processing paradigm in today’s AI engines, 40 , 41 where both the input and the weighting values have to be updated at line rate through time division multiplexing (TDM) approaches until all matrix tiles are processed. To this end, the upgrade of neuromorphic photonics into a versatile AI processing platform has to proceed along the paradigm of today’s TPU and GPU computational engines, where a limited amount of hardware resources can execute DNNs with significantly higher dimensions.…”

Section: Introductionmentioning

confidence: 99%

Neuromorphic silicon photonics with 50 GHz tiled matrix multiplication for deep-learning applications

et al. 2023

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The explosive volume growth of deep-learning (DL) applications has triggered an era in computing, with neuromorphic photonic platforms promising to merge ultra-high speed and energy efficiency credentials with the brain-inspired computing primitives. The transfer of deep neural networks (DNNs) onto silicon photonic (SiPho) architectures requires, however, an analog computing engine that can perform tiled matrix multiplication (TMM) at line rate to support DL applications with a large number of trainable parameters, similar to the approach followed by state-of-the-art electronic graphics processing units. Herein, we demonstrate an analog SiPho computing engine that relies on a coherent architecture and can perform optical TMM at the record-high speed of 50 GHz. Its potential to support DL applications, where the number of trainable parameters exceeds the available hardware dimensions, is highlighted through a photonic DNN that can reliably detect distributed denial-of-service attacks within a data center with a Cohen's kappa score-based accuracy of 0.636.

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Section: Introductionmentioning

confidence: 99%

Neuromorphic silicon photonics with 50 GHz tiled matrix multiplication for deep-learning applications

et al. 2023

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“…Nevertheless, the nature of such a thorough investigation does not align directly with the objectives of the current study. Cutting edge research currently attempts to optimise the architecture and performance of Machine Learning algorithms by exploring solutions ranging from the use of genetic algorithms [11] and dispersing the computational and data processing load to peripheral computers (the Edge) [12], to developing state of the art spatial accelerators to expedite big data processing [13]. Despite the utmost significance of these advancements, Appl.…”

Section: Introduction 1purpose and Innovation Of This Workmentioning

confidence: 99%

Performance Evaluation of Artificial Neural Networks (ANN) Predicting Heat Transfer through Masonry Walls Exposed to Fire

Bakas¹,

Kontoleon²

2021

Applied Sciences

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The multiple benefits Artificial Neural Networks (ANNs) bring in terms of time expediency and reduction in required resources establish them as an extremely useful tool for engineering researchers and field practitioners. However, the blind acceptance of their predicted results needs to be avoided, and a thorough review and assessment of the output are necessary prior to adopting them in further research or field operations. This study explores the use of ANNs on a heat transfer application. It features masonry wall assemblies exposed to elevated temperatures on one side, as generated by the standard fire curve proposed by Eurocode EN1991-1-2. A juxtaposition with previously published ANN development processes and protocols is attempted, while the end results of the developed algorithms are evaluated in terms of accuracy and reliability. The significance of the careful consideration of the density and quality of input data offered to the model, in conjunction with an appropriate algorithm architecture, is highlighted. The risk of misleading metric results is also brought to attention, while useful steps for mitigating such risks are discussed. Finally, proposals for the further integration of ANNs in heat transfer research and applications are made.

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Configurable sparse matrix - matrix multiplication accelerator on FPGA: A systematic design space exploration approach with quantization effects

Noble,

Nalesh,

Kala

et al. 2024

Alexandria Engineering Journal

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Evaluating Spatial Accelerator Architectures with Tiled Matrix-Matrix Multiplication

Cited by 20 publications

References 31 publications

Neuromorphic silicon photonics with 50 GHz tiled matrix multiplication for deep-learning applications

Neuromorphic silicon photonics with 50 GHz tiled matrix multiplication for deep-learning applications

Performance Evaluation of Artificial Neural Networks (ANN) Predicting Heat Transfer through Masonry Walls Exposed to Fire

Configurable sparse matrix - matrix multiplication accelerator on FPGA: A systematic design space exploration approach with quantization effects

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