作者：
Wu, TingfangPan, LinqiangSoochow Univ
Sch Comp Sci & Technol Prov Key Lab Comp Informat Proc Technol Suzhou 215006 Peoples R China Huazhong Univ Sci & Technol
Inst Artificial Intelligence Sch Artificial Intelligence & Automat Key Lab Image Informat Proc & Intelligent Control Wuhan 430074 Peoples R China
Spiking neural P systems with communication on request (SNQP systems) are neurally inspired computing devices, where a neuron actively seeks spikes from presynaptic neurons instead of passively waiting for spikes. In ...
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Spiking neural P systems with communication on request (SNQP systems) are neurally inspired computing devices, where a neuron actively seeks spikes from presynaptic neurons instead of passively waiting for spikes. In this work, we consider SNQP systems with mute rules (SNQPM systems), where mute rules have no communication functioning, namely the application of a mute rule only affects the number of spikes in the neuron where the rule resides, without effect on other neurons. It is demonstrated the computation capability of SNQPM systems with only mute rules does not exceed that of register machines with two registers, thereby not Turing universal. SNQPM systems are Turing universal when both mute rules and request rules are employed. Furthermore, two universal SNQPM systems with 7 neurons or 13 neurons are constructed as devices of number generating and function computing, respectively. Comparing to the universal SNQP system with 14 neurons and two types of spikes, SNQPM systems show the capability of trading-off mute rules and the types of spikes.
A high-performance computation platform based on field-programmable gate arrays targets nuclear and particle physics experiment applications. The system can be constructed or scaled into a supercomputer-equivalent siz...
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A high-performance computation platform based on field-programmable gate arrays targets nuclear and particle physics experiment applications. The system can be constructed or scaled into a supercomputer-equivalent size for detector data processing by inserting compute nodes into advanced telecommunications computing architecture (ATCA) crates. Among the case study results are that one ATCA crate can provide a computation capability equivalent to hundreds of commodity PCs for Hades online particle track reconstruction and Cherenkov ring recognition.
To eliminate the bottleneck of computation and communication capability in classic direct position determination, recently several fast approximation methods have been proposed. However, the estimation accuracy of the...
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To eliminate the bottleneck of computation and communication capability in classic direct position determination, recently several fast approximation methods have been proposed. However, the estimation accuracy of these methods is not satisfactory. In this letter, the common theoretical principle behind these fast approximation methods is analysed in depth. It is demonstrated that the maximum eigenvalue of a matrix can be approximated by the matrix norm. The performance of several commonly used matrix norms is then compared, and the Frobenius norm is preferable because of its great potential inferred from the analysis in this letter. Numerical simulations indicate that, compared with conventional methods, the proposed method can achieve better estimation accuracy with less computational complexity.
In recent years, increasing studies have shown that the networks in the brain can reach a critical state where dynamics exhibit a mixture of synchronous and asynchronous firing activity. It has been hypothesized that ...
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ISBN:
(纸本)9783319590721;9783319590714
In recent years, increasing studies have shown that the networks in the brain can reach a critical state where dynamics exhibit a mixture of synchronous and asynchronous firing activity. It has been hypothesized that the homeostatic level balanced between stability and plasticity of this critical state may be the optimal state for performing diverse neural computational tasks. Motivated by this, the role of critical state in neural computation based on liquid state machines (LSM), which is one of the neural network application model of liquid computing, has been investigated in this note. Different from a randomly connect structure in liquid component of LSM in most studies, the synaptic weights among neurons in proposed liquid are refined by spike-timing-dependent plasticity (STDP);meanwhile, the degrees of neurons excitability are regulated to maintain a low average activity level by Intrinsic Plasticity (IP). The results have shown that the network yield maximal computational performance when subjected to critical dynamical states.
Batched sparse (BATS) codes are proposed for transmitting a collection of packets through communication networks employing linear network coding. BATS codes generalize fountain codes and preserve the properties such a...
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ISBN:
(纸本)9781457705953
Batched sparse (BATS) codes are proposed for transmitting a collection of packets through communication networks employing linear network coding. BATS codes generalize fountain codes and preserve the properties such as ratelessness and low encoding/decoding complexity. Moreover, the buffer size and the computation capability of the intermediate network nodes required to apply BATS codes are independent of the number of packets for transmission. It is verified theoretically for certain cases and demonstrated numerically for the general cases that BATS codes achieve rates very close to the capacity of linear operator channels.
With the popularity and development of the Internet of things (IoT), human life has been deeply affected. Because of the limitations of computation capability and battery capacity, it is difficult for IoT devices to s...
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ISBN:
(纸本)9781728199160
With the popularity and development of the Internet of things (IoT), human life has been deeply affected. Because of the limitations of computation capability and battery capacity, it is difficult for IoT devices to support frequent and complex computing. Motivated by this challenge, many works attempt to upload tasks of IoT devices to the cloud center for computation. However, because of the limitation of distance and bandwidth, cloud computing is difficult to guarantee low latency. As a feasible solution, Mobile Edge Computing (MEC) has attracted more and more attention. Most existing works focus on the computation offloading strategy, while the task scheduling on edge servers is not studied in depth. The tasks uploaded by IoT devices are dynamic and random, and there are dependencies between these tasks. Therefore, it is difficult for edge servers to find a task scheduling scheme to minimize the task execution delay. In this paper, to solve the task scheduling problem of edge server in multi-server and multi-user MEC system, we propose a heuristic algorithm based on the following three scenarios: 1) Tasks uploaded by IoT devices is dynamic and uncertain. 2) There are dependencies between tasks. 3) The computation capability of the edge server is limited. Experimental results show that the proposed algorithm can significantly reduce the overall completion time of tasks and the average task execution delay in the edge server.
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