11 citations · 20 across the 5 of their papers we have counts for
6 papers
One Agent per Zone: Decentralised Active Inference under Partial Observability for Multi-Zone HVAC Control on Edge Hardware
Panagiotis Kasnesis, Lazaros Toumanidis, Christos Chatzigeorgiou +2
Multi-zone HVAC control is a shared-resource problem: zones on a floor share one fan and cooling coil, so each zone's demand affects its neighbours and the building's peak power. O…
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…
A Hardware-oriented Approach for Efficient Bayesian Inference Computation and Deployment
Nikola Pižurica, Matteo Risso, Nikola Milović +4
Bayesian inference provides a principled foundation for reasoning under uncertainty, but its computational cost hinders deployment on resource-constrained edge devices. In this pap…
Robustifying the Deployment of tinyML Models for Autonomous mini-vehicles
Miguel de Prado, Manuele Rusci, Romain Donze +4
Standard-size autonomous navigation vehicles have rapidly improved thanks to the breakthroughs of deep learning. However, scaling autonomous driving to low-power systems deployed o…
Automated Design Space Exploration for optimised Deployment of DNN on Arm Cortex-A CPUs
Miguel de Prado, Andrew Mundy, Rabia Saeed +3
The spread of deep learning on embedded devices has prompted the development of numerous methods to optimise the deployment of deep neural networks (DNN). Works have mainly focused…