An important branch of artificial intelligence systems and architectures is Capsule networks (CapsNets) have been known extremely large amount of parameters and computation because of the complex capsule routing algor...
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We address the problem of enforcing global invariants, i.e., system-level properties, in Collective Adaptive systems, such as distributed and decentralized Internet of Things (IoT) solutions. In particular, we propose...
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ISBN:
(纸本)9783031751066;9783031751073
We address the problem of enforcing global invariants, i.e., system-level properties, in Collective Adaptive systems, such as distributed and decentralized Internet of Things (IoT) solutions. In particular, we propose a novel approach adopting Attribute-based memory Updates (AbU), a calculus modeling declarative, event-driven systems with attribute-based communication. Our methodology leverages a combination of precise node-level scheduling and local reasoning, with local invariants derived from the system-level property to guarantee. This distributed and decentralized approach promotes efficient enforcing while ensuring desired system-wide behavior, without the need for a central controlling authority.
As satellite network communication systems become an increasingly pivotal role in modern life, The routine maintenance of satellite networks is challenging due to limited resources and their susceptibility to interfer...
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Designing effective and user-friendly Human-Machine Interfaces for simulators can be a complex endeavor that requires adherence to guidelines established through decades of research. The inherent complexity of the sys...
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Nowadays, energy buildings have a huge impact in society regarding the active role in the management of energy consumption. Hence, building owners are required to avoid energy losses and improve energy efficiency as h...
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ISBN:
(纸本)9783031820724;9783031820731
Nowadays, energy buildings have a huge impact in society regarding the active role in the management of energy consumption. Hence, building owners are required to avoid energy losses and improve energy efficiency as high as possible. Therefore, it is required to plan an optimization strategy to buy and sell energy in the market ahead of time. To formulate this optimization plan, building owners require the work of specialists responsible for processing, training, forecasting, and evaluation tasks regarding the prediction of energy consumption data from a building for a specific target of time. Therefore, a multiagent-system is needed to allow the cooperation of various agents including the building owner, forecast provider, data structurer and error analysis. Moreover, forecasting algorithms such as artificial neural networks should be taken into consideration in order to process large quantities of energy consumption data during the training and forecasting phases.
POSIT offers a wider dynamic range when compared to floating-point (FP) formats with lesser number of bits. Such data formats are required to address the need for low-bit high-precision hardware architectures for neur...
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ISBN:
(纸本)9798350330991;9798350331004
POSIT offers a wider dynamic range when compared to floating-point (FP) formats with lesser number of bits. Such data formats are required to address the need for low-bit high-precision hardware architectures for neural networks (NNs) on edge platforms. Activation functions (Af) which introduce non-linearity during the feature extraction process remain as a core component for realizing NN systems. CORDIC (COordinate Rotation Digital computer) architecture is a hardware efficient technique to realize complex non-linear functions and is deemed suitable to implement Afs. Hence, this work aims to investigate POSIT data formatted CORDIC architecture to realize Afs (Tanh, Sigmoid and Softmax) in different architectural styles. A benchmark evaluation for the proposed POSIT data formatted Afs with the improved CORDIC architecture over SOTA (IEEE 754 FP formats) based designs are presented. The noticeable improvement in hardware design space and error metrics makes the CORDIC architecture-based POSIT formatted Afs stand out over other methods. All the design files are made publicly available for easy adoption and further usage to the designers' and researchers' community.
Training and deploying large-scale machine learning models is time-consuming, requires significant distributed computing infrastructures, and incurs high operational costs. Our analysis, grounded in real-world large m...
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ISBN:
(纸本)9798350326598;9798350326581
Training and deploying large-scale machine learning models is time-consuming, requires significant distributed computing infrastructures, and incurs high operational costs. Our analysis, grounded in real-world large model training on datacenter-scale infrastructures, reveals that 14 similar to 32% of all GPU hours are spent on communication with no overlapping computation. To minimize this outstanding communication latency and other inherent at-scale inefficiencies, we introduce an agile performance modeling framework, MAD-Max. This framework is designed to optimize parallelization strategies and facilitate hardware-software co-design opportunities. Through the application of MAD-Max to a suite of real-world large-scale ML models on state-of-the-art GPU clusters, we showcase potential throughput enhancements of up to 2.24x for pre-training and up to 5.27x for inference scenarios, respectively.
The proceedings contain 8 papers. The special focus in this conference is on distributedcomputer and Communication networks. The topics include: Transient Behavior of the Photonic Switch with Duplicati...
ISBN:
(纸本)9783031618345
The proceedings contain 8 papers. The special focus in this conference is on distributedcomputer and Communication networks. The topics include: Transient Behavior of the Photonic Switch with Duplication of Switching Elements in the All-Optical Network with Heterogeneous Traffic;examining the Performance of a distributed System Through the Application of Queuing Theory;constructive Approach to Multi-position Passive Acoustic Localization in Information-Measurement systems;broadband Wireless networks Based on Tethered High-Altitude Unmanned Platforms;FPGA Implementation of a Decoder with Low-Density Parity Checks Based on the Minimum Sum Algorithm for 5G networks;Selecting the Performance Metrics to Control the CPU Oversubscription Ratio in a Cloud Server.
Workflow systems provide a convenient way for users to write large-scale applications by composing independent tasks into large graphs that can be executed concurrently on high-performance clusters. In many newer work...
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ISBN:
(纸本)9798400704130
Workflow systems provide a convenient way for users to write large-scale applications by composing independent tasks into large graphs that can be executed concurrently on high-performance clusters. In many newer workflow systems, tasks are often expressed as a combination of function invocations in a high-level language. Because necessary code and data are not statically known prior to execution, they must be moved into the cluster at runtime. An obvious way of doing this is to translate function invocations into self-contained executable programs and run them as usual, but this brings a hefty performance penalty: a function invocation now needs to piggyback its context with extra code and data to a remote node, and the remote node needs to take extra time to reconstruct the invocation's context before executing it, both detrimental to lightweight short-running functions. A better solution for workflow systems is to treat functions and invocations as first-class abstractions: subsequent invocations of the same function on a worker node should only pay for the cost of context setup once and reuse the context between different invocations. The remaining problems lie in discovering, distributing, and retaining the reusable context among workers. In this paper, we discuss the rationale and design requirement of these mechanisms to support context reuse, and implement them in TaskVine, a data-intensive distributed framework and execution engine. Our results from executing a large-scale neural network inference application and a molecular design application show that treating functions and invocations as first-class abstractions reduces the execution time of the applications by 94.5% and 26.9%, respectively.
This paper proposes a novel multi-agent framework for penetration testing that aims to enable efficient and adaptive collaboration of specialised agents. This framework uses the Blackboard system for communication bet...
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