Amirreza Yousefzadeh is an Assistant Professor specializing in computer architecture design for embedded systems. His research focuses on hardware acceleration for artificial intelligence, particularly in energy-efficient neuromorphic computing and edge AI applications. Research Interests Neuromorphic computing architectures Event-driven AI hardware Sparsity exploitation in neural networks Embedded vision systems Digital circuit design for AI Research Trends Recent work (2024-2025) demonstrates expertise in spiking neural networks (SNNs), activation sparsity, and hardware-software co-design for neuromorphic processors. Key areas include object detection, energy efficiency optimization, and digital implementations of synaptic delays. Technical Contributions Developed SENMap for multi-objective data-flow mapping Created SENSIM simulator for multi-core neuromorphic systems Investigated 3D stacking for memory-dominated architectures Explored temporal sparsity in event-based processing
- Artificial Intelligence
- Edge AI
- Sensors
- +۷ مورد دیگر


