5 citations · 8 across the 7 of their papers we have counts for
11 papers
Small-World Communication Fabrics for Neuromorphic Multicore-SoCs
Sebastian Billaudelle, Christian Metzner, Jimmy Weber +7
As neuromorphic systems scale beyond a single core, inter-core event communication can become a dominant contributor to memory footprint, latency, and energy consumption. Biologica…
A Linear Implementation of an Analog Resonate-and-Fire Neuron
Angqi Liu, Filippo Moro, Sebastian Billaudelle +1
Oscillatory dynamics have recently proven highly effective in machine learning (ML), particularly through State-Space-Models (SSM) that leverage structured linear recurrences for l…
Unified Memcapacitor-Memristor Memory for Synaptic Weights and Neuron Temporal Dynamics
Simone D'Agostino, Marco Massarotto, Tristan Torchet +7
We present a fabricated and experimentally characterized memory stack that unifies memristive and memcapacitive behavior. Exploiting this dual functionality, we design a circuit en…
Quantizing Small-Scale State-Space Models for Edge AI
Leo Zhao, Tristan Torchet, Melika Payvand +2
State-space models (SSMs) have recently gained attention in deep learning for their ability to efficiently model long-range dependencies, making them promising candidates for edge-…
MINIMALIST: switched-capacitor circuits for efficient in-memory computation of gated recurrent units
Sebastian Billaudelle, Laura Kriener, Filippo Moro +2
Recurrent neural networks (RNNs) have been a long-standing candidate for processing of temporal sequence data, especially in memory-constrained systems that one may find in embedde…
The Role of Temporal Hierarchy in Spiking Neural Networks
Filippo Moro, Pau Vilimelis Aceituno, Laura Kriener +1
Spiking Neural Networks (SNNs) have the potential for rich spatio-temporal signal processing thanks to exploiting both spatial and temporal parameters. The temporal dynamics such a…
DenRAM: Neuromorphic Dendritic Architecture with RRAM for Efficient Temporal Processing with Delays
Simone DAgostino, Filippo Moro, Tristan Torchet +5
An increasing number of neuroscience studies are highlighting the importance of spatial dendritic branching in pyramidal neurons in the brain for supporting non-linear computation…
Synaptic metaplasticity with multi-level memristive devices
Simone D'Agostino, Filippo Moro, Tifenn Hirtzlin +5
Deep learning has made remarkable progress in various tasks, surpassing human performance in some cases. However, one drawback of neural networks is catastrophic forgetting, where…
Dendritic Computation through Exploiting Resistive Memory as both Delays and Weights
Melika Payvand, Simone D'Agostino, Filippo Moro +3
Biological neurons can detect complex spatio-temporal features in spiking patterns via their synapses spread across across their dendritic branches. This is achieved by modulating…
Hardware calibrated learning to compensate heterogeneity in analog RRAM-based Spiking Neural Networks
Filippo Moro, E. Esmanhotto, T. Hirtzlin +10
Spiking Neural Networks (SNNs) can unleash the full power of analog Resistive Random Access Memories (RRAMs) based circuits for low power signal processing. Their inherent computat…
PCM-trace: Scalable Synaptic Eligibility Traces with Resistivity Drift of Phase-Change Materials
Yigit Demirag, Filippo Moro, Thomas Dalgaty +5
Dedicated hardware implementations of spiking neural networks that combine the advantages of mixed-signal neuromorphic circuits with those of emerging memory technologies have the…