arXiv subjects

Lorenzo Posani

Publications and source records attributed to Lorenzo Posani.

2 recordsLinked to original sources

arXiv2019

The carbon footprint of distributed cloud storage

The ICT (Information Communication Technologies) ecosystem is estimated to be responsible, as of today, for 10% of the total worldwide energy demand - equivalent to the combined energy production of Germany and Japan. Cloud storage, mainly operated through large and densely-packed data centers, constitutes a non-negligible part of it. However, since the cloud is a fast-inflating market and the energy-efficiency of data centers is mostly an insensitive issue for the collectivity, its carbon footprint shows no signs of slowing down. In this paper, we analyze a novel paradigm for cloud storage (implemented by Cubbit, http://cubbit.io), in which data are stored and distributed over a network of p2p-interacting ARM-based single-board devices. We compare Cubbit's distributed cloud to the traditional centralized solution in terms of environmental footprint and energy efficiency. We demonstrate that, compared to the centralized cloud, the distributed architecture of Cubbit has a carbon footprint reduced of a 77% factor for data storage and of a 50% factor for data transfers. These results provide an example of how a radical paradigm shift in a large-reach technology can benefit both the final consumer as well as our society as a whole.

cs.DC↗

arXiv2017

Statistical Physics and Representations in Real and Artificial Neural Networks

This document presents the material of two lectures on statistical physics and neural representations, delivered by one of us (R.M.) at the Fundamental Problems in Statistical Physics XIV summer school in July 2017. In a first part, we consider the neural representations of space (maps) in the hippocampus. We introduce an extension of the Hopfield model, able to store multiple spatial maps as continuous, finite-dimensional attractors. The phase diagram and dynamical properties of the model are analyzed. We then show how spatial representations can be dynamically decoded using an effective Ising model capturing the correlation structure in the neural data, and compare applications to data obtained from hippocampal multi-electrode recordings and by (sub)sampling our attractor model. In a second part, we focus on the problem of learning data representations in machine learning, in particular with artificial neural networks. We start by introducing data representations through some illustrations. We then analyze two important algorithms, Principal Component Analysis and Restricted Boltzmann Machines, with tools from statistical physics.

physics.data-an↗