In order to keep up with the increasing volume and challenging requirements of Cyber-Physical systems (CPS), model-based design and simulation-based verification are becoming essential development practices. This stud...
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This book highlights new trends and challenges in agent systems, and new digital and knowledge economy research, and includes 34 papers on areas such as intelligent agent interaction and collaboration, modeling, simul...
ISBN:
(纸本)9783319920306
This book highlights new trends and challenges in agent systems, and new digital and knowledge economy research, and includes 34 papers on areas such as intelligent agent interaction and collaboration, modeling, simulation and mobile agents, agent communication and social networks, business Informatics, design and implementation of intelligent agents and multi-agent systems. These papers were presented at the 12th international KES conference on Agents and Multi-Agent systems: Technologies and Applications (KES-AMSTA 2018) held on Australias Gold Coast. The modern economy is driven by technologies and knowledge. Digital technologies can free, shift and multiply choices, often intruding on the space of other industries, by providing new ways of conducting business operations and creating values for customers and companies. The book addresses topics that contribute to the modern digital economy, including software agents, multi-agent systems, agent modeling, mobile and cloud computing, big data analysis, business intelligence, artificial intelligence, social systems, computerembeddedsystems and nature inspired manufacturing, which contribute to the modern digital economy. The results presented are of theoretical and practical value to researchers and industrial practitioners working in the fields of artificial intelligence, collective computational intelligence, innovative business models, new digital and knowledge economy and, in particular, agent and multi-agent systems, technologies, tools and applications.
In this era, the requirement of high-performance computing at low power cost can be met by the parallel execution of an application on a large number of programmable cores. Emerging many-core architectures provide den...
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In this era, the requirement of high-performance computing at low power cost can be met by the parallel execution of an application on a large number of programmable cores. Emerging many-core architectures provide dense interconnection fabrics leading to new communication requirements. In particular, the effective exploitation of synchronous and asynchronous channels for fast communication from/to internal cores and external devices is a key issue for these architectures. In this paper, we propose a methodology for clustering sequential commands used for configuring the parallel execution of tasks on a globally asynchronous locally synchronous multi-chip many-core neuromorphic platform. With the purpose of reducing communication costs and maximise the exploitation of the available communication bandwidth, we adapted the Multiple Sequence Alignment (MSA) algorithm for clustering the unicast streams of packets used for the configuration of each core so as to generate a coherent multicast stream that configures all cores at once. In preliminary experiments, we demonstrate how the proposed method can lead up to a 97% reduction in packet transmission thus positively affecting the overall communication cost.
In recent years, there has been a growing interest on relaxing the pessimistic DRAM refresh rate due to the incurred power and throughput loss. Undeniably, a critical factor in determining the refresh rate relaxation ...
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ISBN:
(纸本)9781538634370
In recent years, there has been a growing interest on relaxing the pessimistic DRAM refresh rate due to the incurred power and throughput loss. Undeniably, a critical factor in determining the refresh rate relaxation that can be achieved lies on the degree of the DRAM error-rate deterioration that is incurred and on the amount of estimated errors that can be handled by system mitigation schemes which are mainly being evaluated in simulators. To estimate the DRAM faults under relaxed refresh, the majority of the existing works rely on estimated DRAM failure probability models using only the spatial distribution of the DRAM retention time across the memory cells. We observe that such failure models have neglected the intricate dependence on the memory accesses, which inherently refresh the accessed rows. In this paper, we propose that the intervals between consecutive accesses must also be considered during DRAM simulation. We show that the estimation of the distribution of accesses poses a lot of challenges mainly due to the time consuming full system simulations that are required. To address such challenges, this paper presents one of the first efforts to model the access time-dependent DRAM retention time by developing a fast simulation infrastructure based on binary instrumentation. The basic idea behind the proposed approach lies on the quantification of the time elapsed between consecutive memory accesses on the same row and its relation to the DRAM failure probability, which is then being used for a more accurate fault injection. The introduced overheads of the instrumentation functions are measured during native execution allowing accurate corrections of the time elapsed between consecutive accesses. The efficacy of our framework is being evaluated using various artificial benchmarks. Results show that our scheme helps to limit the misprediction of estimated errors of current error-injection models.
The proceedings contain 29 papers. The special focus in this conference is on Artificial General Intelligence. The topics include: Unsupervised language learning in opencog;Functionalist emotion model in NARS;towards ...
ISBN:
(纸本)9783319976754
The proceedings contain 29 papers. The special focus in this conference is on Artificial General Intelligence. The topics include: Unsupervised language learning in opencog;Functionalist emotion model in NARS;towards a sociological conception of artificial intelligence;efficient concept formation in large state spaces;DSO cognitive architecture: Implementation and validation of the global workspace enhancement;the foundations of deep learning with a path towards general intelligence;zeta distribution and transfer learning problem;Vision system for AGI: Problems and directions;semantic image retrieval by uniting deep neural networks and cognitive architectures;request confirmation networks in micropsi 2;the temporal singularity: Time-accelerated simulated civilizations and their implications;a computational theory for life-long learning of semantics;cumulative learning with causal-relational models;Transforming kantian aesthetic principles into qualitative hermeneutics for contemplative AGI agents;Towards general evaluation of intelligent systems: Using semantic analysis to improve environments in the AIQ test;Perception from an AGI perspective;a phenomenologically justifiable simulation of mental modeling;a time-critical simulation of language comprehension;how failure facilitates success;adaptive compressed search;task analysis for teaching cumulative learners;associative memory: An spiking neural network robotic implementation;A comprehensive ethical framework for AI entities: Foundations;partial operator induction with beta distributions;solving tree problems with category theory;goal-directed procedure learning;can machines design? An artificial general intelligence approach.
Implementing self-adaptive embeddedsystems, such as UAVs, involves an offline provisioning of the several implementations of the embedded functionalities with different characteristics in resource usage and performan...
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ISBN:
(纸本)9781538678800
Implementing self-adaptive embeddedsystems, such as UAVs, involves an offline provisioning of the several implementations of the embedded functionalities with different characteristics in resource usage and performance in order for the system to dynamically adapt itself under uncertainties. FPGA-based architectures offer for support for high flexibility with dynamic reconfiguration features. We propose an autonomic control architecture for self-adaptive and self- reconfigurable FPGA-based embeddedsystems. The control architecture is structured in three layers: a mission manager, a reconfiguration manager and a scheduling manager. In this work we focus on the design of the reconfiguration manager. We propose a design approach using automata-based discrete control. It involves reactive programming that provides formal semantics, and discrete controller synthesis from declarative objectives.
The modern economy is driven by technologies and knowledge. Digital technologies can free, shift and multiply choices, often intruding on the space of other industries, by providing new ways of conducting business ope...
ISBN:
(纸本)9783319819884
The modern economy is driven by technologies and knowledge. Digital technologies can free, shift and multiply choices, often intruding on the space of other industries, by providing new ways of conducting business operations and creating values for customers and companies. The topics covered in this volume include software agents, multi-agent systems, agent modelling, mobile and cloud computing, big data analysis, business intelligence, artificial intelligence, social systems, computerembeddedsystems and nature inspired manufacturing, etc. that contribute to the modern Digital Economy. This volume highlights new trends and challenges in agent, new digital and knowledge economy research and includes 28 papers classified in the following specific topics: business process management, agent-based modeling and simulation, anthropic-oriented computing, learning paradigms, business informatics and gaming, digital economy, and advances in networked virtual enterprises. Published papers were selected for presentation at the 10th KES conference on Agent and Multi-Agent systems: Technologies and Applications (KES-AMSTA 2016) held in Puerto de la Cruz, Tenerife, Spain. Presented results would be of theoretical and practical value to researchers and industrial practitioners working in the fields of artificial intelligence, collective computational intelligence, innovative business models, new digital and knowledge economy and, in particular, agent and multi-agent systems, technologies, tools and applications.
Internet-of-Things (IoT) is the technical backbone of smart cities which are envisioned to cope up with rapid urbanization of human population with limited resources. IoT provides three key features of smart cities su...
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Internet-of-Things (IoT) is the technical backbone of smart cities which are envisioned to cope up with rapid urbanization of human population with limited resources. IoT provides three key features of smart cities such as intelligence, interconnection, and instrumentation. IoT is essentially a system-of-systems which can be considered as a configurable dynamic global network of networks. The main components of IoT include the following: 1) The Things; 2) Internet; 3) LAN; and 4) The Cloud. IoT is built by various diverse components including electronics, sensors, actuators, controllers, networks, firmware, and software. However, the existing electronics, controllers, and processors do not meet IoT requirements, such as multiple sensors, communication protocols, and security requirements. The existing computer-aided design (CAD) or electronic design automation tools are not enough to meet diverse challenges such as time-to-market, complexity, and cost of IoT. The required electronic circuits and systems need to be developed by handling and solving specific requirements. Real-time and ultralow power plays a major role since mobile devices in the IoT have to provide a long availability with a relative small energy budget. At the same time, reliability, availability, real-time constraints, and performance requirements pose significant challenges, and therefore, lead to a high interest in research. In this special issue, different approaches to design novel devices, circuits, and systems for solving the challenges with IoT are targeted. Various novel design automation components including modeling, design flows, simulation methods, and optimizations for designing of modern IoT are targeted, from system level down to device level. The current special issue was envisioned with the above technical considerations. After a rigorous review process, a set of articles were selected for this special issue. These papers are briefly discussed in the rest of the editorial.
This paper presents a set of methods for effective compression of 3D-CNN deep learning architectures with a particular focus on embedded platforms. Deep learning models are massive regarding the number of parameters, ...
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This paper presents a set of methods for effective compression of 3D-CNN deep learning architectures with a particular focus on embedded platforms. Deep learning models are massive regarding the number of parameters, especially in video processing. Such memory consumption poses a challenge when it comes to efficient deployment to embedded devices. The authors applied a series of quantization and pruning techniques and showed that it is possible to significantly reduce an 80M-parameters-large deep learning model with a negligible (approx. 1 perc.) decrease in accuracy.
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