3 citations · 4 across the 8 of their papers we have counts for
9 papers
Bio-inspired Learning and Decision-Making with Probabilistic In-Memory Computing Hardware: Part 2
Thomas Dalgaty, Eiji Kawasaki, Miguel de Prado +3
This report extends our previous work (Part 1), which introduced an energy-based model for learning and decision-making under uncertainty. The model leverages stochastic Langevin d…
Bio-inspired Learning and Decision-Making with Probabilistic In-Memory Computing Hardware: Part 1
Thomas Dalgaty, Eiji Kawasaki, Miguel de Prado +2
Learning and decision-making in animals are often modeled as Bayesian processes, where sensory evidence is integrated with prior beliefs to guide behavior in the face of uncertaint…
Hardware-accelerated graph neural networks: an alternative approach for neuromorphic event-based audio classification and keyword spotting on SoC FPGA
Kamil Jeziorek, Piotr Wzorek, Krzysztof Blachut +6
As the volume of data recorded by embedded edge sensors increases, particularly from neuromorphic devices producing discrete event streams, there is a growing need for hardware-awa…
Bayesian continual learning and forgetting in neural networks
Djohan Bonnet, Kellian Cottart, Tifenn Hirtzlin +4
Biological synapses effortlessly balance memory retention and flexibility, yet artificial neural networks still struggle with the extremes of catastrophic forgetting and catastroph…
Hardware-Accelerated Event-Graph Neural Networks for Low-Latency Time-Series Classification on SoC FPGA
Hiroshi Nakano, Krzysztof Blachut, Kamil Jeziorek +6
As the quantities of data recorded by embedded edge sensors grow, so too does the need for intelligent local processing. Such data often comes in the form of time-series signals, b…
Bayesian Metaplasticity from Synaptic Uncertainty
Djohan Bonnet, Tifenn Hirtzlin, Tarcisius Januel +3
Catastrophic forgetting remains a challenge for neural networks, especially in lifelong learning scenarios. In this study, we introduce MEtaplasticity from Synaptic Uncertainty (ME…