Evangelos Stromatias
University of Manchester, Instituto de Microelectrónica de Sevilla
Papers
2
Total Citations
109
H-Index
2
About
Evangelos Stromatias is a leading researcher in neuromorphic computing, with a focus on energy-efficient neural networks and spike-based visual recognition. His work bridges the gap between biological plausibility and practical hardware implementation. In his highly cited 2013 paper (72 citations), he conducted a pioneering power analysis of large-scale, real-time spiking neural networks on the SpiNNaker platform, demonstrating that custom neuromorphic systems can achieve orders-of-magnitude energy savings over supercomputers while maintaining flexibility. This work established critical benchmarks for comparing neuromorphic hardware. Subsequently, his 2016 study (37 citations) introduced a standardized dataset and evaluation framework for spike-based visual recognition, addressing a key bottleneck in the field by enabling fair comparisons of neuromorphic algorithms. Stromatias’s contributions have been instrumental in advancing low-power, brain-inspired computing, making him a key figure in the transition from theoretical spiking networks to practical, energy-efficient systems for real-world applications like edge AI and robotics.
Research Focus
Key Achievements
Top Papers
- 1Power analysis of large-scale, real-time neural networks on SpiNNaker72 citations · 2013
- 2Benchmarking Spike-Based Visual Recognition: A Dataset and Evaluation37 citations · 2016