International Journal of Reciprocal Symmetry and Theoretical Physics

Peer Reviewed Article

Vol. 6 (2019)

High-Performance VLSI Architectures for Artificial Intelligence and Machine Learning Applications

Submitted
24 March 2024
Published
15-03-2019

Abstract

Artificial intelligence (AI) and machine learning (ML) applications are accelerated by high-performance VLSI architectures, which allow for real-time inference, analysis, and decision-making across a wide range of disciplines. The design, development, and implementation of VLSI architectures for AI and ML applications are examined in this paper, with an emphasis on scalability, efficiency, and practicality. The study's primary goals are to examine architectural paradigms, optimization strategies, energy-efficient design concepts, performance evaluation approaches, and practical uses of high-performance VLSI architectures for AI and ML. A thorough analysis of the body of research, case studies, and policy implications about VLSI design for AI and ML applications are all part of the methodology. Principal discoveries emphasize the variety of architectural paradigms, optimization strategies, and practical uses of high-performance VLSI architectures, along with their implementation difficulties and policy ramifications. The significance of ethical deliberations, adherence to regulations, and international cooperation in guaranteeing the conscientious and fair application of artificial intelligence and machine learning is highlighted by policy ramifications. By offering insights into the design, optimization, deployment, and policy implications of high-performance VLSI architectures for AI and ML applications, this study advances our collective understanding of these technologies and the field of AI-driven technologies.

References

  1. Ande, J. R. P. K. (2018). Performance-Based Seismic Design of High-Rise Buildings: Incorporating Nonlinear Soil-Structure Interaction Effects. Engineering International, 6(2), 187–200. https://doi.org/10.18034/ei.v6i2.691
  2. Ande, J. R. P. K., Varghese, A., Mallipeddi, S. R., Goda, D. R., & Yerram, S. R. (2017). Modeling and Simulation of Electromagnetic Interference in Power Distribution Networks: Implications for Grid Stability. Asia Pacific Journal of Energy and Environment, 4(2), 71-80. https://doi.org/10.18034/apjee.v4i2.720
  3. Bing, Z., Meschede, C., Röhrbein, F., Huang, K., Knoll, A. C. (2018). A Survey of Robotics Control Based on Learning-Inspired Spiking Neural Networks. Frontiers in Neurorobotics. https://doi.org/10.3389/fnbot.2018.00035
  4. Elnaggar, R., Chakrabarty, K. (2018). Machine Learning for Hardware Security: Opportunities and Risks. Journal of Electronic Testing: (JETTA), 34(2), 183-201. https://doi.org/10.1007/s10836-018-5726-9
  5. Goda, D. R. (2016). A Fully Analytical Back-gate Model for N-channel Gallium Nitrate MESFET's with Back Channel Implant. California State University, Northridge. http://hdl.handle.net/10211.3/176151
  6. Goda, D. R., Yerram, S. R., & Mallipeddi, S. R. (2018). Stochastic Optimization Models for Supply Chain Management: Integrating Uncertainty into Decision-Making Processes. Global Disclosure of Economics and Business, 7(2), 123-136. https://doi.org/10.18034/gdeb.v7i2.725
  7. Grout, I., Mullin, L. (2018). Hardware Considerations for Tensor Implementation and Analysis Using the Field Programmable Gate Array. Electronics, 7(11), 320. https://doi.org/10.3390/electronics7110320
  8. Li, Z., Wang, Y., Zhi, T., Chen, T. (2017). A Survey of Neural Network Accelerators. Frontiers of Computer Science, 11(5), 746-761. https://doi.org/10.1007/s11704-016-6159-1
  9. Mahadasa, R. (2016). Blockchain Integration in Cloud Computing: A Promising Approach for Data Integrity and Trust. Technology & Management Review, 1, 14-20. https://upright.pub/index.php/tmr/article/view/113
  10. Mahadasa, R. (2017). Decoding the Future: Artificial Intelligence in Healthcare. Malaysian Journal of Medical and Biological Research, 4(2), 167-174. https://mjmbr.my/index.php/mjmbr/article/view/683
  11. Mahadasa, R., & Surarapu, P. (2016). Toward Green Clouds: Sustainable Practices and Energy-Efficient Solutions in Cloud Computing. Asia Pacific Journal of Energy and Environment, 3(2), 83-88. https://doi.org/10.18034/apjee.v3i2.713
  12. Mallipeddi, S. R., & Goda, D. R. (2018). Solid-State Electrolytes for High-Energy-Density Lithium-Ion Batteries: Challenges and Opportunities. Asia Pacific Journal of Energy and Environment, 5(2), 103-112. https://doi.org/10.18034/apjee.v5i2.726
  13. Mallipeddi, S. R., Goda, D. R., Yerram, S. R., Varghese, A., & Ande, J. R. P. K. (2017). Telemedicine and Beyond: Navigating the Frontier of Medical Technology. Technology & Management Review, 2, 37-50. https://upright.pub/index.php/tmr/article/view/118
  14. Mallipeddi, S. R., Lushbough, C. M., & Gnimpieba, E. Z. (2014). Reference Integrator: a workflow for similarity driven multi-sources publication merging. The Steering Committee of the World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp). https://www.proquest.com/docview/1648971371
  15. Mandouh, E. E., Wassal, A. G. (2018). Application of Machine Learning Techniques in Post-Silicon Debugging and Bug Localization. Journal of Electronic Testing: (JETTA), 34(2), 163-181. https://doi.org/10.1007/s10836-018-5716-y
  16. Meng, Y., Yang, Y., Chung, H., Pil-Ho, L., Shao, C. (2018). Enhancing Sustainability and Energy Efficiency in Smart Factories: A Review. Sustainability, 10(12), 4779. https://doi.org/10.3390/su10124779
  17. Neftci, E. O., Augustine, C., Paul, S., Detorakis, G. (2017). Event-Driven Random Back-Propagation: Enabling Neuromorphic Deep Learning Machines. Frontiers in Neuroscience. https://doi.org/10.3389/fnins.2017.00324
  18. Ro, Y., Lee, E., Ahn, J. H. (2018). Evaluating the Impact of Optical Interconnects on a Multi-Chip Machine-Learning Architecture. Electronics, 7(8). https://doi.org/10.3390/electronics7080130
  19. Sarigül, M., Avci, M. (2018). Performance Comparison of Different Momentum Techniques on Deep Reinforcement Learning. Journal of Information and Telecommunication, 2(2), 216. https://doi.org/10.1080/24751839.2018.1440453
  20. Surarapu, P. (2016). Emerging Trends in Smart Grid Technologies: An Overview of Future Power Systems. International Journal of Reciprocal Symmetry and Theoretical Physics, 3, 17-24. https://upright.pub/index.php/ijrstp/article/view/114
  21. Surarapu, P. (2017). Security Matters: Safeguarding Java Applications in an Era of Increasing Cyber Threats. Asian Journal of Applied Science and Engineering, 6(1), 169–176. https://doi.org/10.18034/ajase.v6i1.82
  22. Surarapu, P., & Mahadasa, R. (2017). Enhancing Web Development through the Utilization of Cutting-Edge HTML5. Technology & Management Review, 2, 25-36. https://upright.pub/index.php/tmr/article/view/115
  23. Surarapu, P., Mahadasa, R., & Dekkati, S. (2018). Examination of Nascent Technologies in E-Accounting: A Study on the Prospective Trajectory of Accounting. Asian Accounting and Auditing Advancement, 9(1), 89–100. https://4ajournal.com/article/view/83
  24. Tao, J-H., Du, Z-D., Guo, Q., Lan, H-Y, Zhang, L. (2018). BenchIP: Benchmarking Intelligence Processors. Journal of Computer Science and Technology, 33(1), 1-23. https://doi.org/10.1007/s11390-018-1805-8
  25. Tuli, F. A., Varghese, A., & Ande, J. R. P. K. (2018). Data-Driven Decision Making: A Framework for Integrating Workforce Analytics and Predictive HR Metrics in Digitalized Environments. Global Disclosure of Economics and Business, 7(2), 109-122. https://doi.org/10.18034/gdeb.v7i2.724
  26. Yerram, S. R., & Varghese, A. (2018). Entrepreneurial Innovation and Export Diversification: Strategies for India’s Global Trade Expansion. American Journal of Trade and Policy, 5(3), 151–160. https://doi.org/10.18034/ajtp.v5i3.692