Municipal transitions: The social, energy, and spatial dynamics of sociotechnical change in South Tyrol, Italy

With the aim of proposing recommendations on how to use social and territorial specificities as levers for wider achievement of climate and energy targets at local level, this research analyses territories as sociotechnical systems. Defining the territory as a sociotechnical system allows us to underline the interrelations between space, energy and society. Groups of municipalities in a region can be identified with respect to their potential production of renewable energy by means of well-known data-mining approaches. Similar municipalities linking together can share ideas and promote collaborations, supporting clever social planning in the transition towards a new energy system. The methodology is applied to the South Tyrol case study (Italy). Results show eight different spatially-based sociotechnical systems within the coherent cultural and institutional context of South Tyrol. In particular, this paper observes eight different systems in terms of (1) different renewable energy source preferences in semi-urban and rural contexts; (2) different links with other local planning, management, and policy needs; (3) different socio-demographic specificities of individuals and families; (4) presence of different kinds of stakeholders or of (5) different socio-spatial organizations based on land cover. Each energy system has its own specificities and potentialities, including social and spatial dimensions, that can address a more balanced, inclusive, equal, and accelerated energy transition at the local and translocal scale

Toolflows for Mapping Convolutional Neural Networks on FPGAs: A Survey and Future Directions

In the past decade, Convolutional Neural Networks (CNNs) have demonstrated state-of-the-art performance in various Artificial Intelligence tasks. To accelerate the experimentation and development of CNNs, several software frameworks have been released, primarily targeting power-hungry CPUs and GPUs. In this context, reconfigurable hardware in the form of FPGAs constitutes a potential alternative platform that can be integrated in the existing deep learning ecosystem to provide a tunable balance between performance, power consumption and programmability. In this paper, a survey of the existing CNN-to-FPGA toolflows is presented, comprising a comparative study of their key characteristics which include the supported applications, architectural choices, design space exploration methods and achieved performance. Moreover, major challenges and objectives introduced by the latest trends in CNN algorithmic research are identified and presented. Finally, a uniform evaluation methodology is proposed, aiming at the comprehensive, complete and in-depth evaluation of CNN-to-FPGA toolflows.Comment: Accepted for publication at the ACM Computing Surveys (CSUR) journal, 201

Performance analysis and optimization of automotive GPUs

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Advanced Driver Assistance Systems (ADAS) and Autonomous Driving (AD) have drastically increased the performance demands of automotive systems. Suitable highperformance platforms building upon Graphic Processing Units (GPUs) have been developed to respond to this demand, being NVIDIA Jetson TX2 a relevant representative. However, whether high-performance GPU configurations are appropriate for automotive setups remains as an open question. This paper aims at providing light on this question by modelling an automotive GPU (Jetson TX2), analyzing its microarchitectural parameters against relevant benchmarks, and identifying specific configurations able to meaningfully increase performance within similar cost envelopes, or to decrease costs preserving original performance levels. Overall, our analysis opens the door to the optimization of automotive GPUs for further system efficiency.This work has been partially supported by the Spanish Ministry of Economy and Competitiveness (MINECO) under grant TIN2015-65316-P, the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 772773) and the HiPEAC Network of Excellence. Pedro Benedicte and Jaume Abella have been partially supported by the MINECO under FPU15/01394 grant and Ramon y Cajal postdoctoral fellowship number RYC-2013-14717 respectively and Leonidas Kosmidis under Juan de la Cierva-Formacin postdoctoral fellowship (FJCI-2017-34095).Peer ReviewedPostprint (author's final draft

Autonomy: a review and a reappraisal

In the field of artificial life there is no agreement on what defines ‘autonomy’. This makes it difficult to measure progress made towards understanding as well as engineering autonomous systems. Here, we review the diversity of approaches and categorize them by introducing a conceptual distinction between behavioral and constitutive autonomy. Differences in the autonomy of artificial and biological agents tend to be marginalized for the former and treated as absolute for the latter. We argue that with this distinction the apparent opposition can be resolved