Athanasios Vasilopoulos
Papers
1
Total Citations
22
H-Index
1
About
Dr. Athanasios Vasilopoulos is a leading researcher in energy-efficient AI hardware, specializing in in-memory computing and neuromorphic systems. His work addresses the critical challenge of enabling low-power, autonomously learning AI for edge devices, where traditional models falter due to high computational demands. Vasilopoulos’s most notable contribution is pioneering a “learning-to-learn” approach with phase-change memory-based in-memory computing, as detailed in his highly cited 2025 paper (22 citations). This work demonstrates rapid, on-device adaptation without extensive fine-tuning, significantly reducing energy consumption while maintaining accuracy. His research bridges machine learning and device physics, offering a path toward sustainable, real-time AI deployment in resource-constrained environments. With a growing citation impact, Vasilopoulos is recognized for pushing the boundaries of hardware-software co-design, making him a key figure in the next generation of autonomous, edge-compatible AI systems.
Research Focus
Key Achievements
Top Papers
- 1