Reconfigurable Mobile Multimedia Systems

This paper discusses reconfigurability issues in lowpower hand-held multimedia systems, with particular emphasis on energy conservation. We claim that a radical new approach has to be taken in order to fulfill the requirements - in terms of processing power and energy consumption - of future mobile applications. A reconfigurable systems-architecture in combination with a QoS driven operating system is introduced that can deal with the inherent dynamics of a mobile system. We present the preliminary results of studies we have done on reconfiguration in hand-held mobile computers: by having reconfigurable media streams, by using reconfigurable processing modules and by migrating functions

The Glasgow raspberry pi cloud: a scale model for cloud computing infrastructures

Data Centers (DC) used to support Cloud services often consist of tens of thousands of networked machines under a single roof. The significant capital outlay required to replicate such infrastructures constitutes a major obstacle to practical implementation and evaluation of research in this domain. Currently, most research into Cloud computing relies on either limited software simulation, or the use of a testbed environments with a handful of machines. The recent introduction of the Raspberry Pi, a low-cost, low-power single-board computer, has made the construction of a miniature Cloud DCs more affordable. In this paper, we present the Glasgow Raspberry Pi Cloud (PiCloud), a scale model of a DC composed of clusters of Raspberry Pi devices. The PiCloud emulates every layer of a Cloud stack, ranging from resource virtualisation to network behaviour, providing a full-featured Cloud Computing research and educational environment

Industrial R&D in Italy: Exploration and Exploitation Strategies in Industrial R&D

This paper discusses four types of strategic decisions in technology management in established firms. It is well known that deciding between exploration and exploitation in R&D, and eventually combining the two strategies, is a crucial issue. However, we argue that more attention, both from a theoretical and an empirical perspective, should be paid to the strategic solutions which are implemented as a consequence of such decisions, as well as to the various types of interactions between strategic decisions and organizational solutions in industrial R&D. Here we apply to R&D management concepts derived from the industrial dynamics literature, and use a theoretical framework to describe and analyse four case studies concerning the largest R&D centres of Italian firms operating in different industrial sectors (telecommunications, automotive, communication and cables, and semiconductors). The different approaches that those private R&D centres have chosen in their recent past are compared and discussed. More specifically, we try and analyze the patterns of exploration, technology transfer and commercialization that industrial R&D labs have adopted in order to combine short-term objectives of exploitation of research results and competencies, and long-term goals of exploration of new technological trajectories. The proposed approach is based upon the use of two dimensions: first, the type of technological change, and second the control of complementary assets and the existence of a dominant design. We argue that the interpretation of the four case studies can represent a useful basis for discussion among R&D managers as well as innovation and technology management scholars.

Memory and information processing in neuromorphic systems

A striking difference between brain-inspired neuromorphic processors and current von Neumann processors architectures is the way in which memory and processing is organized. As Information and Communication Technologies continue to address the need for increased computational power through the increase of cores within a digital processor, neuromorphic engineers and scientists can complement this need by building processor architectures where memory is distributed with the processing. In this paper we present a survey of brain-inspired processor architectures that support models of cortical networks and deep neural networks. These architectures range from serial clocked implementations of multi-neuron systems to massively parallel asynchronous ones and from purely digital systems to mixed analog/digital systems which implement more biological-like models of neurons and synapses together with a suite of adaptation and learning mechanisms analogous to the ones found in biological nervous systems. We describe the advantages of the different approaches being pursued and present the challenges that need to be addressed for building artificial neural processing systems that can display the richness of behaviors seen in biological systems.Comment: Submitted to Proceedings of IEEE, review of recently proposed neuromorphic computing platforms and system

Real time unsupervised learning of visual stimuli in neuromorphic VLSI systems

Neuromorphic chips embody computational principles operating in the nervous system, into microelectronic devices. In this domain it is important to identify computational primitives that theory and experiments suggest as generic and reusable cognitive elements. One such element is provided by attractor dynamics in recurrent networks. Point attractors are equilibrium states of the dynamics (up to fluctuations), determined by the synaptic structure of the network; a `basin' of attraction comprises all initial states leading to a given attractor upon relaxation, hence making attractor dynamics suitable to implement robust associative memory. The initial network state is dictated by the stimulus, and relaxation to the attractor state implements the retrieval of the corresponding memorized prototypical pattern. In a previous work we demonstrated that a neuromorphic recurrent network of spiking neurons and suitably chosen, fixed synapses supports attractor dynamics. Here we focus on learning: activating on-chip synaptic plasticity and using a theory-driven strategy for choosing network parameters, we show that autonomous learning, following repeated presentation of simple visual stimuli, shapes a synaptic connectivity supporting stimulus-selective attractors. Associative memory develops on chip as the result of the coupled stimulus-driven neural activity and ensuing synaptic dynamics, with no artificial separation between learning and retrieval phases.Comment: submitted to Scientific Repor

Industrial R&D in Italy: What are new dynamics of exploitation and exploration?

This paper aims at exploring the dynamics of industrial R&D activities in large companies. Through the use of four case studies of the largest R&D centers of Italian firms operating in different industrial sectors (telecommunications, automotive, rubber and plastics, and semiconductors), we try and compare the different approaches that private R&D centers have chosen in the recent past, to face the challenges of growing complexity in their research areas and increasing constraints in budgets devoted to R&D activities. The difficulties Italian companies face in the management of their R&D investments have to do with the specificities of a fairly weak national innovation system as well as with challenges that are common to other national and industrial contexts.