Exploring the Utilization of VLSI Devices and Circuits in the Context of AI Applications through an Extensive Investigation
Keywords:
AI, Integrating VLSI, Neural Networks,Abstract
This research aims to delve into the utilization of VLSI (Very Large Scale Integration) devices and circuits within the realm of Artificial Intelligence (AI) applications. Through an extensive investigation, this study explores the integration of VLSI technology to enhance the efficiency, speed, and performance of AI systems. The research investigates various aspects such as the design, implementation, and optimization of VLSI circuits tailored specifically for AI algorithms and applications. Additionally, the study examines the impact of VLSI devices on power consumption, area utilization, and overall system scalability in the context of AI. The findings from this research contribute to a deeper understanding of the role of VLSI devices and circuits in advancing AI technology and provide valuable insights for future developments in this field.
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References
T.M. Austin, S. Yalamanchili, and D. Fick, "VLSI for Artificial Intelligence" published in Proceedings of the IEEE, (2019).
J. Cong et al., "Designing VLSI for Machine Learning and AI" published in Proceedings of the IEEE,(2020).
A. Shastri et al., "Neuromorphic Computing with Integrated Photonic Circuits" published in Nature, (2021).
R. S. Shenoy et al., Emerging Memory Technologies for AI Hardware Accelerators" published in ACM Transactions on Embedded Computing Systems, (2020).
K. Hwang et al., Energy-Efficient VLSI Architectures for Convolutional Neural Networks" published in IEEE Transactions on Very Large Scale Integration (VLSI) Systems, (2017).
P. Li et al., Towards Zero-Shot Learning with Hierarchical Direct Feedback Alignment" published in Proceedings of the 37th International Conference on Machine Learning (ICML), (2020).
S. Han et al., "Survey of Emerging Technologies for Artificial Intelligence Accelerators" published in IEEE Journal on Emerging and Selected Topics in Circuits and Systems, (2019).
D. B. Strukov et al., "Energy-Efficient Neuromorphic Computing" published in Nature, (2020).
V. Sze et al., "Efficient Inference Engines for Deep Neural Networks: A Survey" published in IEEE Journal on Emerging and Selected Topics in Circuits and Systems, (2017).
C. Bartolozzi et al.,"Neuromorphic Hardware Systems for AI and Robotics: A Review" published in Frontiers in Neuroscience, (2021).
Y. Cao et al., "Enabling Technologies for Edge AI: A Comprehensive Survey” published in IEEE Transactions on Cognitive and Developmental Systems, (2021).
Y. Cheng et al., Energy-Efficient Deep Learning: A Comprehensive Survey" published in IEEE Transactions on Neural Networks and Learning Systems, (2021).
M. S. Hossain et al., "Hardware Accelerators for Deep Learning: A Comprehensive Survey" published in ACM Computing Surveys, (2021).
M. Sze et al., "Toward Efficient Processing of Deep Neural Networks: A Survey of Architectures and Algorithms" published in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, (2017).
V. M. Patel et al., "Energy-Efficient Computing for Deep Learning: A Review" published in ACM Computing Surveys, (2020).
W. Song et al.,"Analog Computing Using Emerging Nonvolatile Memory Devices for In-Memory Neural Network Acceleration" published in Proceedings of the IEEE, (2020).
G. Indiveri et al., "Brain-Inspired Computing Paradigms: A Comprehensive Overview" published in Proceedings of the IEEE, 2019.
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