NEUROMORPHIC COMPUTING: Rethinking Computing through Brain-Inspired Architectures
At the SMILIES group, we explore neuromorphic computing as a radical approach to overcoming the limitations of conventional architectures. By drawing inspiration from the structure and functionality of the human brain, our research aims to design energy-efficient, robust, and adaptive computing platforms tailored for edge intelligence and embedded AI.
Spiking neural networks
Hardware accelerations
Brain inspired learning
Brain inspired relability
Event based vision
Funded projects
WISE4.0 project
Web 4.0—driven by decentralisation, intelligent automation, immersive experiences, and context-aware services—will reshape most application domains including healthcare, energy, mobility, education, and media. It demands infrastructures with real-time responsiveness, trust, data integrity, and distributed intelligence.
NEUROPULS project
NEUROPULS pioneers a new era in edge computing, addressing the escalating demand for localized data processing. By harnessing neuromorphic computing principles and integrating advanced photonic security layers, NEUROPULS offers unprecedented energy efficiency, reduced latency, and enhanced security. This revolutionary project introduces novel materials for synapses/neurons and on-chip spiking sources, culminating in RISC-V compliant interfaces for seamless adaptability. With a state-of-the-art simulation platform, NEUROPULS is poised to achieve a remarkable two-fold increase in energy efficiency, setting a new standard for edge-computing systems. Join us in shaping a future where computing power meets efficiency and security head-on.
Repositories on GitHub
- Spiker: FPGA hardware accelerator for Spiking Neural Networks (SNN)
- SpikExplorer: Automatic Design Space Exploration (DSE) for Spiking Neural Networks (SNN)
- SpikingJET: Automatic fault-injection in Spiking Neural Networks (SNN)
- Continual learning in SNN:Optimized continual learning in Spiking Neural Networks (SNN)
Publications
- A. Carpegna, A. Savino and S. Di Carlo, “Spiker: an FPGA-optimized Hardware accelerator for Spiking Neural Networks,” 2022 IEEE Computer Society Annual Symposium on VLSI (ISVLSI), Nicosia, Cyprus, 2022, pp. 14-19. https://doi.org/10.1109/ISVLSI54635.2022.00016
- Padovano, D.; Carpegna, A.; Savino, A.; Di Carlo, S. SpikeExplorer: Hardware-Oriented Design Space Exploration for Spiking Neural Networks on FPGA. Electronics 2024, 13, 1744. https://doi.org/10.3390/electronics13091744
- B. Göğebakan, E. Magliano, A. Carpegna, A. Ruospo, A. Savino and S. D. Carlo, “SpikingJET: Enhancing Fault Injection for Fully and Convolutional Spiking Neural Networks,” 2024 IEEE 30th International Symposium on On-Line Testing and Robust System Design (IOLTS), Rennes, France, 2024, pp. 1-7. https://doi.org/10.1109/IOLTS60994.2024.10616060
Involved team
