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Posts by Collection
portfolio
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publications
Hardware calibrated learning to compensate heterogeneity in analog RRAM-based Spiking Neural Networks
Published in ISCAS 2022, 2022
Recommended citation: F. Moro. (2022). "PHardware calibrated learning to compensate heterogeneity in analog RRAM-based Spiking Neural Networks." ISCAS. 1(3).
Mosaic: in-memory computing and routing for small-world spike-based neuromorphic systems
Published in Nature Communications, 2024
Recommended citation: M. Payvand et al. (2024). "Mosaic: in-memory computing and routing for small-world spike-based neuromorphic systems." Nature Communications. 1(3).
DenRAM: neuromorphic dendritic architecture with RRAM for efficient temporal processing with delays
Published in Nature Communications, 2024
The Role of Temporal Hierarchy in Spiking Neural Networks
Published in ArXiv, 2024
Recommended citation: F. Moro et al. (2024). "The Role of Temporal Hierarchy in Spiking Neural Networks." ArXiv. 1(2).
talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Introduction to Neuromorphic Engineerings
Graduate course at ETH, ETH, 2023
This lecture introduced the core principles of neuromorphic engineering, focusing on the design of real-time, adaptive, and energy-efficient systems that emulate the brain. It explored how sparsity, locality, and event-based computation underpin biological efficiency and how these are translated into hardware. The talk presented neuromorphic sensors, analog and digital implementations, and in-memory computing as enabling technologies. Special emphasis was placed on training and architecture strategies for event-driven systems, including the Mosaic framework and delay-based SNNs. Finally, it highlighted how physical dynamics and enriched computational units can enhance neuromorphic processing.
Advanced technology for Neuromorphic Systems
Graduate course at ETH, ETH, 2024
This lecture traced the evolution of transistor technology from planar CMOS to cutting-edge nanosheet architectures, emphasizing their implications for neuromorphic computing. It analyzed short-channel effects and how FinFETs and FDSOI help mitigate them. The talk then explored the memory technology landscape, from traditional SRAM/DRAM to emerging RRAM, MRAM, and PCM. Special focus was given to In-Memory Computing (IMC), especially its synergy with neuromorphic systems. Finally, the challenges and solutions around variability in RRAM-based spiking neural networks were discussed.