VeNNus: An Artificial Intelligence Accelerator Based on RISC-V Architecture

Harini, S.; Ravikumar, Aswathy; Garg, Dhruv

doi:10.1007/978-981-15-8767-2_25

Cited by 4 publications

(2 citation statements)

References 14 publications

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“…A detailed analysis of the implementation of the CNN model in TPU and GPU is done on different benchmark applications ( Ravikumar et al, 2022 ). The hardware accelerators efficiently process many machine learning algorithms in a single-node, multimode, and cloud environments ( Harini & Ravikumar, 2021 ; Harini, Ravikumar & Garg, 2021 ; Harini, Ravikumar & Keshwani, 2022 ).…”

Section: Related Workmentioning

confidence: 99%

Real-time pneumonia prediction using pipelined spark and high-performance computing

Ravikumar¹,

Sriraman²

2023

PeerJ Computer Science

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Background Pneumonia is a respiratory disease caused by bacteria; it affects many people, particularly in impoverished countries where pollution, unclean living standards, overpopulation, and insufficient medical infrastructures are prevalent. To guarantee curative therapy and boost survival chances, it is vital to detect pneumonia soon enough. Imaging using chest X-rays is the most common way of detecting pneumonia. However, analyzing chest X-rays is a complex process vulnerable to subjective variation. Moreover, the data available is growing exponentially, and it will take hours and days to train the model to predict pneumonia. Timely prediction is significant to guarantee a better cure and treatment. Existing work provided by different authors needs more precision, and the computation time for predicting pneumonia is also much longer. Therefore, there is a requirement for early forecasting. Using X-ray picture samples, the system must have a continuous and unsupervised learning system for early diagnosis. Methods In this article, the training time of the model is accelerated using the distributed data-parallel approach and the computational power of high-performance computing devices. This research aims to diagnose pneumonia using X-ray pictures with more precision, greater speed, and fewer processing resources. Distributed deep learning techniques are gaining popularity owing to the rising need for computational resources for deep learning models with several parameters. In contrast to conventional training methods, data-parallel training enables several compute nodes to train massive deep-learning models to improve training efficiency concurrently. Deploying the model in Spark solves the scalability and acceleration. Spark’s distributed processing capability reads data from multiple nodes, and the results demonstrate that training time can be drastically reduced by utilizing these techniques, which is a significant necessity when dealing with large datasets. Results The proposed model makes the prediction 1.5 times faster than the traditional CNN model used for pneumonia prediction. The model also achieved an accuracy of 98.72%. The speed-up varying from 1.2 to 1.5 was obtained in the synchronous and asynchronous parallel model. The speed-up is reduced in the parallel asynchronous model due to the presence of straggler nodes.

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Section: Related Workmentioning

confidence: 99%

Real-time pneumonia prediction using pipelined spark and high-performance computing

Ravikumar¹,

Sriraman²

2023

PeerJ Computer Science

Self Cite

View full text Add to dashboard Cite

show abstract

“…Since the bulk of training time has been spent on data transmission, this behavior drastically limits the advantage that may be gained via parallel training. Adopting high-performance hardware accelerators simply reduces the overhead associated with computing while keeping the overhead associated with communication unaltered [ [8] , [9] , [10] ]. This results in a more significant proportion of time being spent on communication.…”

Section: Introductionmentioning

confidence: 99%