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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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| Paper Title: | Exploring Efficient Neural Architectures for Large-Scale Data Analysis |
| Authors Name: | Ankush Jitendrakumar Tyagi |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_205535
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| Published Paper Id: | IJRTI2507170 |
| Published In: | Volume 10 Issue 7, July-2025 |
| DOI: | https://doi.org/10.56975/ijrti.v10i7.205535 |
| Abstract: | This paper will discuss the optimization tenets and technological tools of effective neural architecture that can contribute to enormous volumes of data. The emergence of deep learning as a revolutionizing means of data-driven activities has been accompanied by the rediscovery of some of the issues pertinent to earlier neural network employments, specifically to the scalability, the calculations burden, the latency, and energy costs in the processing of large and high- dimensional data. To alleviate such fears, there has been great advancement in the design of neural architectures, which can achieve the same performance with less consumption of resources. The most important architecture ideas, including depth-wise separable convolutions, residual connections, and attention mechanisms, will be discussed in detail to see how they simplify the model and drive its speed. Moreover, the role of model compression solutions such as pruning, quantisation, and knowledge distillation is mentioned in terms of keeping the same predictive performance with a minimal computing burden. One of the approaches that can be used is Neural Architecture Search (NAS), which is explored as a technique to automatically discover the best model architectures relevant to the peculiarities of a certain dataset. Moreover, the support of hardware-aware design featuring special processors such as GPUs, TPUs, and neuromorphic chips is assessed to illustrate how the parallel evolution of architecture and hardware makes high-performance computing possible. Lastly, practical applications across fields like climate modeling, image recognition, and personalized suggestions show that efficient architectures are quite applicable and effective in real-life applications. The article will provide an organised access to the current trends in effective neural design and pay particular attention to the fact that they are increasingly becoming relevant when it comes to scalable, accurate, and resource-aware processing of complex data. |
| Keywords: | Efficient Neural Architectures, Large-Scale Data Analysis, Model Compression Techniques, Neural Architecture Search (NAS), Hardware-Aware Design |
| Cite Article: | "Exploring Efficient Neural Architectures for Large-Scale Data Analysis", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 7, page no.b482-b491, July-2025, Available :http://www.ijrti.org/papers/IJRTI2507170.pdf |
| Downloads: | 000417 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
| Publication Details: |
Published Paper ID: IJRTI2507170
Registration ID:205535
Published In: Volume 10 Issue 7, July-2025
DOI (Digital Object Identifier): https://doi.org/10.56975/ijrti.v10i7.205535
Page No: b482-b491 Country: Hayward, California, United States Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2507170 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2507170 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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