211 citations · 629 across the 21 of their papers we have counts for
26 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…
NORACL: Neurogenesis for Oracle-free Resource-Adaptive Continual Learning
Karthik Charan Raghunathan, Christian Metzner, Laura Kriener +1
In a continual learning setting, we require a model to be plastic enough to learn a new task and stable enough to not disturb previously learned capabilities. We argue that this di…
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…
mGRADE: Minimal Recurrent Gating Meets Delay Convolutions for Lightweight Sequence Modeling
Tristan Torchet, Christian Metzner, Karthik Charan Raghunathan +4
Multi-timescale sequence modeling relies on capturing both local fast dynamics and global slow context; yet, maintaining these capabilities under the strict memory constraints comm…
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…
Hardware architecture and routing-aware training for optimal memory usage: a case study
Jimmy Weber, Theo Ballet, Melika Payvand
Efficient deployment of neural networks on resource-constrained hardware demands optimal use of on-chip memory. In event-based processors, this is particularly critical for routing…
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…
DelGrad: Exact event-based gradients for training delays and weights on spiking neuromorphic hardware
Julian Göltz, Jimmy Weber, Laura Kriener +5
Spiking neural networks (SNNs) inherently rely on the timing of signals for representing and processing information. Incorporating trainable transmission delays, alongside synaptic…
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…
Neuromorphic analog circuits for robust on-chip always-on learning in spiking neural networks
Arianna Rubino, Matteo Cartiglia, Melika Payvand +1
Mixed-signal neuromorphic systems represent a promising solution for solving extreme-edge computing tasks without relying on external computing resources. Their spiking neural netw…
Scaling Limits of Memristor-Based Routers for Asynchronous Neuromorphic Systems
Junren Chen, Siyao Yang, Huaqiang Wu +2
Multi-core neuromorphic systems typically use on-chip routers to transmit spikes among cores. These routers require significant memory resources and consume a large part of the ove…
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…
A 120dB Programmable-Range On-Chip Pulse Generator for Characterizing Ferroelectric Devices
Shyam Narayanan, Erika Covi, Viktor Havel +7
Novel non-volatile memory devices based on ferroelectric thin films represent a promising emerging technology that is ideally suited for neuromorphic applications. The physical swi…
Stochastic dendrites enable online learning in mixed-signal neuromorphic processing systems
Matteo Cartiglia, Arianna Rubino, Shyam Narayanan +4
The stringent memory and power constraints required in edge-computing sensory-processing applications have made event-driven neuromorphic systems a promising technology. On-chip on…
Online Training of Spiking Recurrent Neural Networks with Phase-Change Memory Synapses
Yigit Demirag, Charlotte Frenkel, Melika Payvand +1
Spiking recurrent neural networks (RNNs) are a promising tool for solving a wide variety of complex cognitive and motor tasks, due to their rich temporal dynamics and sparse proces…
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…
Adaptive Extreme Edge Computing for Wearable Devices
Erika Covi, Elisa Donati, Hadi Heidari +4
Wearable devices are a fast-growing technology with impact on personal healthcare for both society and economy. Due to the widespread of sensors in pervasive and distributed networ…
On-Chip Error-triggered Learning of Multi-layer Memristive Spiking Neural Networks
Melika Payvand, Mohammed E. Fouda, Fadi Kurdahi +2
Recent breakthroughs in neuromorphic computing show that local forms of gradient descent learning are compatible with Spiking Neural Networks (SNNs) and synaptic plasticity. Althou…
Ultra-Low-Power FDSOI Neural Circuits for Extreme-Edge Neuromorphic Intelligence
Arianna Rubino, Can Livanelioglu, Ning Qiao +2
Recent years have seen an increasing interest in the development of artificial intelligence circuits and systems for edge computing applications. In-memory computing mixed-signal n…
Hardware Implementation of Deep Network Accelerators Towards Healthcare and Biomedical Applications
Mostafa Rahimi Azghadi, Corey Lammie, Jason K. Eshraghian +4
The advent of dedicated Deep Learning (DL) accelerators and neuromorphic processors has brought on new opportunities for applying both Deep and Spiking Neural Network (SNN) algorit…