Enhancing the Proficiency of Artificial Neural Network on Prediction with GPU

Singh, Neeru; Panda, Supriya P.

doi:10.1109/comitcon.2019.8862440

Cited by 6 publications

(2 citation statements)

References 12 publications

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“…But before the backward pass, the delta function kernel is launch so that it can be used to updates weights and bias in simultaneous accessing mode using the multithreaded environment of GPU. Figure 1 shows the parallelism in the GPU grid for artificial neural networks, representing the number of blocks in one grid and the number of threads in one block [25].…”

Section: Methodsmentioning

confidence: 99%

Heterogeneous computing with graphical processing unit: improvised back-propagation algorithm for water level prediction

Singh

Panda

2022

IJECE

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<p>A multitude of research has been rising for predicting the behavior of different real-world problems through machine learning models. An erratic nature occurs due to the augmented behavior and inadequacy of the prerequisite dataset for the prediction of water level over different fundamental models that show flat or low-set accuracy. In this paper, a powerful scaling strategy is proposed for improvised back-propagation algorithm using parallel computing for groundwater level prediction on graphical processing unit (GPU) for the Faridabad region, Haryana, India. This paper aims to propose the new streamlined form of a back-propagation algorithm for heterogeneous computing and to examine the coalescence of artificial neural network (ANN) with GPU for predicting the groundwater level. twenty years of data set from 2001-2020 has been taken into consideration for three input parameters namely, temperature, rainfall, and water level for predicting the groundwater level using parallelized backpropagation algorithm on compute unified device architecture (CUDA). This employs the back-propagation algorithm to be best suited to reinforce learning and performance by providing more accurate and fast results for water level predictions on GPUs as compared to sequential ones on central processing units (CPUs) alone.</p>

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Section: Methodsmentioning

confidence: 99%

Heterogeneous computing with graphical processing unit: improvised back-propagation algorithm for water level prediction

Singh

Panda

2022

IJECE

Self Cite

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“…As every neuron is sequentially computed with CPUs, the system workload is significantly increased. Even though GPUs lead the computation parallelization [15][16][17], the power consumption is also increased.…”

Section: Introductionmentioning

confidence: 99%

Intellino: Processor for Embedded Artificial Intelligence

et al. 2020

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The development of computation technology and artificial intelligence (AI) field brings about AI to be applied to various system. In addition, the research on hardware-based AI processors leads to the minimization of AI devices. By adapting the AI device to the edge of internet of things (IoT), the system can perform AI operation promptly on the edge and reduce the workload of the system core. As the edge is influenced by the characteristics of the embedded system, implementing hardware which operates with low power in restricted resources on a processor is necessary. In this paper, we propose the intellino, a processor for embedded artificial intelligence. Intellino ensures low power operation based on optimized AI algorithms and reduces the workload of the system core through the hardware implementation of a neural network. In addition, intellino’s dedicated protocol helps the embedded system to enhance the performance. We measure intellino performance, achieving over 95% accuracy, and verify our proposal with an field programmable gate array (FPGA) prototyping.

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