Sitemap
A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
Posts
Three Levels of Biological Inspiration: Bridging NeuroAI and Neuromorphic Engineering
Published:
Artificial Intelligence is advancing at ever increasing speed, learning from massive data and running in huge datacenters. Modern AI does not look much like biological intelligence. So what role does neuroscience play in the context of AI? This question was at the center of a recent workshop organized at Cosyne 2026: “Biologically-Inspired Artificial Intelligence”. Here I show my perspective on this intriguing research question, structuring inspiration from biology into three distinct levels: mechanical, system-level, and behavioral. As a neuromorphic engineer, I am used to working at the mechanical level, replicating the biophysics of neurons and synapses with electronics. However, I see great opportunities to work at a higher level of abstraction by drawing inspiration from biological computation. This hints at the unification of NeuroAI with Neuromorphic Computing.
The Memory Technology Landscape
Published:
Memory technology has become the major battleground for integrated chip innovation. Our chips’ integrated memory is ever-growing and now occupies a considerable portion of commercial silicon dies. While Static-Random-Access-Memory technology dominates the on-chip memory market, a recent article pointed out that SRAM’s scaling - fueled so far by Moore’s Law - is hitting a wall. TSMC’s 3nm node only provides a minor 5% improvement in SRAM bitcell density compared to the 5nm node. So what is the future for on-chip memory?
Memristor-Aware-Training for Resilient Neural Networks
Published:
Memristors: a controversial technology with as many supporters as critics. Memristors have attracted considerable attention in the edge-AI industry as a promising memory technology capable of improving energy efficiency and integration density by orders of magnitude compared with standard integrated memory. However, the memristive revolution has been hindered (or delayed) by many technical difficulties. Among these is the problem of variability, i.e., the stochastic behavior memristors exhibit in their conductance during programming. Is it a dealbreaker? This article shows how to address memristive-conductance variability with a particular training methodology inspired by Quantization-Aware-Training, a method used to train heavily quantized networks. The so-called Memristor-Aware-Training introduces memristor-calibrated variability during training so that optimization can adapt to it and converge to a stable weight configuration that tolerates variability.
portfolio
Portfolio item number 1
Published:
Short description of portfolio item number 1
Portfolio item number 2
Published:
Short description of portfolio item number 2
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
Published:
This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown!
Conference Proceeding talk 3 on Relevant Topic in Your Field
Published:
This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
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.