Search results for " Network"
showing 10 items of 6428 documents
A spiking network for spatial memory formation: Towards a fly-inspired ellipsoid body model
2013
Neural centers devoted to spatial memory and path integration were largely studied in rats and in different insect species like ants and bees. In this paper a neural-based model for the formation of a spatial working memory is proposed mirroring some peculiarities of the Drosophila central brain and in particular the ellipsoid body. Simulation results are reported opening the way to applications on roving platforms.
Taking care of everyone’s business: interpreting Sicilian Mafia embedment through spatial network analysis
2022
Mafia-type organisations often have a strong geographical and cultural entrenchment in the territory they belong. However, their analysis as a spatially networked social structure is still missing. A combined socio-spatial network analysis is presented here, through the demise of a large police operation called Operazione Perseo in 2008. This approach is developed in two ways. At first, a visual representation of the social network of this large group of mafiosi embedded in a geographical space is presented. Three main salient territorial features of the network are thus highlighted. A high density of links in some neighbourhoods, as well as connections across different Mandamenti, the terr…
Hawkes processes on networks for crime data
2022
Motivated by the analysis of crime data in Bucaramanga (Colombia), we propose a spatio-temporal Hawkes point process model adapted to events living on linear networks. We first consider a non-parametric modelling strategy, for both the background and the triggering components, and then we include a parametric estimation of the background based on covariates, and a non-parametric one of the triggering effects. Our network model outperforms a planar version, improving the fitting of the self-exciting point process model.
Interaction in Spoken Word Recognition Models: Feedback Helps
2018
Human perception, cognition, and action requires fast integration of bottom-up signals with top-down knowledge and context. A key theoretical perspective in cognitive science is the interactive activation hypothesis: forward and backward flow in bidirectionally connected neural networks allows humans and other biological systems to approximate optimal integration of bottom-up and top-down information under real-world constraints. An alternative view is that online feedback is neither necessary nor helpful; purely feed forward alternatives can be constructed for any feedback system, and online feedback could not improve processing and would preclude veridical perception. In the domain of spo…
Reducing complexity in H.264/AVC motion estimation by using a GPU
2011
H.264/AVC applies a complex mode decision technique that has high computational complexity in order to reduce the temporal redundancies of video sequences. Several algorithms have been proposed in the literature in recent years with the aim of accelerating this part of the encoding process. Recently, with the emergence of many-core processors or accelerators, a new approach can be adopted for reducing the complexity of the H.264/AVC encoding algorithm. This paper focuses on reducing the inter prediction complexity adopted in H.264/AVC and proposes a GPU-based implementation using CUDA. Experimental results show that the proposed approach reduces the complexity by as much as 99% (100x of spe…
CUDA-enabled Sparse Matrix–Vector Multiplication on GPUs using atomic operations
2013
We propose the Sliced Coordinate Format (SCOO) for Sparse Matrix-Vector Multiplication on GPUs.An associated CUDA implementation which takes advantage of atomic operations is presented.We propose partitioning methods to transform a given sparse matrix into SCOO format.An efficient Dual-GPU implementation which overlaps computation and communication is described.Extensive performance comparisons of SCOO compared to other formats on GPUs and CPUs are provided. Existing formats for Sparse Matrix-Vector Multiplication (SpMV) on the GPU are outperforming their corresponding implementations on multi-core CPUs. In this paper, we present a new format called Sliced COO (SCOO) and an efficient CUDA i…
Finding near-perfect parameters for hardware and code optimizations with automatic multi-objective design space explorations
2012
Summary In the design process of computer systems or processor architectures, typically many different parameters are exposed to configure, tune, and optimize every component of a system. For evaluations and before production, it is desirable to know the best setting for all parameters. Processing speed is no longer the only objective that needs to be optimized; power consumption, area, and so on have become very important. Thus, the best configurations have to be found in respect to multiple objectives. In this article, we use a multi-objective design space exploration tool called Framework for Automatic Design Space Exploration (FADSE) to automatically find near-optimal configurations in …
First Experiences on an Accurate SPH Method on GPUs
2017
It is well known that the standard formulation of the Smoothed Particle Hydrodynamics is usually poor when scattered data distribution is considered or when the approximation near the boundary occurs. Moreover, the method is computational demanding when a high number of data sites and evaluation points are employed. In this paper an enhanced version of the method is proposed improving the accuracy and the efficiency by using a HPC environment. Our implementation exploits the processing power of GPUs for the basic computational kernel resolution. The performance gain demonstrates the method to be accurate and suitable to deal with large sets of data.
Hardware-accelerated spike train generation for neuromorphic image and video processing
2014
Recent studies concerning Spiking Neural Networks show that they are a powerful tool for multiple applications as pattern recognition, image tracking, and detection tasks. The basic functional properties of SNN reside in the use of spike information encoding as the neurons are specifically designed and trained using spike trains. We present a novel and efficient frequency encoding algorithm with Gabor-like receptive fields using probabilistic methods and targeted to FPGA for online pro-cessing. The proposed encoding is versatile, modular and, when applied to images, it is able to perform simple image transforms as edge detection, spot detection or removal, and Gabor-like filtering without a…
FPGA implementation of Spiking Neural Networks supported by a Software Design Environment
2011
Abstract This paper is focused on the creation of Spiking Neural Networks (SNN) in hardware due to their advantages for certain problem solving and their similarity to biological neural system. One of the main uses of this neural structure is pattern classification. The chosen model for the spiking neuron is the Spike Response Model (SRM). For SNN design and implementation, a software application has been developed to provide easy creation, simulation and automatic generation of the hardware model. VHDL was used for the hardware model. This paper describes the functionality of SNN and the design procedure followed to obtain a working neural system in both software and hardware. Designed VHD…