ADVANCING AI HARDWARE ARCHITECTURE: PERFORMANCE ANALYSIS OF CARBON-BASED SEMICONDUCTORS IN HIGH-PERFORMANCE COMPUTING APPLICATIONS
Keywords:
Carbon-based Semiconductors, Artificial Intelligence Hardware, Quantum Computing Integration, Edge Computing Optimization, Neural Network AccelerationAbstract
The advent of artificial intelligence has exposed fundamental limitations in traditional silicon-based semiconductor technologies, necessitating innovative solutions for next-generation computing architectures. This comprehensive article examines the potential of carbon-based semiconductors, particularly graphene and carbon nanotubes (CNTs), in addressing the challenges posed by modern AI workloads. These materials exhibit exceptional electrical, thermal, and mechanical properties that significantly surpass conventional silicon semiconductors, enabling faster processing speeds, reduced energy consumption, and enhanced miniaturization capabilities. The article explores the fundamental characteristics of carbon-based materials, their integration into existing semiconductor architectures, and the challenges involved in scaling production for commercial applications. Particular attention is given to the role of these advanced materials in improving AI hardware performance, especially in edge computing, inference operations, and neural network training. Through analysis of current research developments, manufacturing challenges, and industry adoption patterns, this study provides insights into the timeline for mainstream implementation of carbon-based semiconductors and their potential to revolutionize the computing industry.
References
C. Gajiwala, "The Evolution of AI: How Deep Learning is Transforming Industries," IEEE Trans. Neural Netw. Learn. Syst. https://www.ibtimes.co.in/evolution-ai-how-deep-learning-transforming-industries-876028
H. W. Kim, "Overcoming the Limitations of Si-CMOS and Von Neumann Architectures by Adopting Beyond-Si Devices and Near-Memory Computation," IEEE Trans. Emerg. Topics Comput. https://deepblue.lib.umich.edu/bitstream/handle/2027.42/194508/heewoo_1.pdf?sequence=1
Aneeqa Bashir, Azka Mehvish and Maria Khalil, "Advanced Carbon Materials for Sustainable and Emerging Applications," IntechOpen, 2021. https://www.intechopen.com/chapters/78704
Chetan Choppali Sudarshan, “ECO-CHIP: Estimation of Carbon Footprint of Chiplet-based Architectures for Sustainable VLSI," IEEE International Symposium on High-Performance Computer Architecture (HPCA), 2024. https://ieeexplore.ieee.org/abstract/document/10476457
Mukul Lokhande, "Flex-PE: Flexible and SIMD Multi-Precision Processing Element for AI Workloads," IEEE Trans. Very Large Scale Integr. (VLSI) Syst., 2024. https://arxiv.org/html/2412.11702v1
Hyun-Hwa Choi, "Evaluating the Performance of Deep Learning Inference Service on Edge Platform," Int. Conf. Information and Communication Technology Convergence (ICTC), 2021. https://ieeexplore.ieee.org/document/9620870
Mukul Lokhande et al., "Scale, Scope, and Speed—Managing the Challenges of Multiproduct Manufacturing” https://ieeexplore.ieee.org/document/5398980
Ching-Yao Huang, "Development of a simplified carbon footprinting methodology for the semiconductor industry," https://ieeexplore.ieee.org/document/6360425
S.H. Lavington, "Hardware Implementation of AI Primitives," IET Conference Publication, IEEE Xplore, 1989. https://ieeexplore.ieee.org/document/197996
Yuriy P. Kondratenko, "Research Tendencies and Prospect Domains for AI Development and Implementation," River Publishers eBooks, IEEE Xplore, 2024. https://ieeexplore.ieee.org/book/10631820
William H. Dutton, "Transforming Enterprise: The Economic and Social Implications of Information Technology," IEEE Xplore, 2004. https://ieeexplore.ieee.org/book/6267388
Albert Chun-Chen Liu, "Artificial Intelligence Hardware Design: Challenges and Solutions," IEEE eBooks, 2021. https://ieeexplore.ieee.org/book/9536220
