Angelos Kyriakos

National and Kapodistrian University of Athens

Papers

Total Citations

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About

Angelos Kyriakos is a researcher at the forefront of hardware-accelerated artificial intelligence, specializing in the design of high-performance architectures for deep learning. His work directly addresses the critical challenge of bridging the gap between the computational demands of neural networks and the limitations of conventional processors. Kyriakos’s most influential contribution, his 2019 paper "High Performance Accelerator for CNN Applications," has garnered 25 citations, establishing a foundation for efficient, real-time AI inference in resource-constrained environments. By focusing on hardware-implemented AI, he has enabled the deployment of complex computer vision and natural language processing systems beyond the cloud—into edge devices, autonomous systems, and embedded platforms. His research is pivotal for students and engineers seeking to understand how custom digital circuits can unlock the full potential of neural networks, reducing latency and power consumption while maintaining high accuracy. Kyriakos’s work stands as a key reference for anyone exploring the intersection of VLSI design and machine learning, marking him as a notable contributor to the next generation of intelligent hardware.