1.1k citations · 4.6k across the 68 of their papers we have counts for
24 papers · 1 filter
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…
Spike-based local synaptic plasticity: A survey of computational models and neuromorphic circuits
Lyes Khacef, Philipp Klein, Matteo Cartiglia +3
Understanding how biological neural networks carry out learning using spike-based local plasticity mechanisms can lead to the development of powerful, energy-efficient, and adaptiv…
Cortical-inspired placement and routing: minimizing the memory resources in multi-core neuromorphic processors
Vanessa R. C. Leite, Zhe Su, Adrian M. Whatley +1
Brain-inspired event-based neuromorphic processing systems have emerged as a promising technology in particular for bio-medical circuits and systems. However, both neuromorphic and…
ReckOn: A 28nm Sub-mm2 Task-Agnostic Spiking Recurrent Neural Network Processor Enabling On-Chip Learning over Second-Long Timescales
Charlotte Frenkel, Giacomo Indiveri
A robust real-world deployment of autonomous edge devices requires on-chip adaptation to user-, environment- and task-induced variability. Due to on-chip memory constraints, prior…
A hardware-software co-design approach to minimize the use of memory resources in multi-core neuromorphic processors
Vanessa R. C. Leite, Zhe Su, Adrian M. Whatley +1
Both in electronics and biology, physical implementations of neural networks have severe energy and memory constraints. We propose a hardware-software co-design approach for minimi…
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…