Investigating the Suitability of FPAAs for Evolved Hardware Spiking Neural Networks

Rocke, Patrick; McGinley, Brian; Maher, John; Morgan, Frank; Harkin, Jim

doi:10.1007/978-3-540-85857-7_11

Cited by 21 publications

(11 citation statements)

References 14 publications

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“…Adding configurability comes at a high price in terms of hardware resources, due to various hardwarespecific limitations, such as physical size and essentially two-dimensional structure. So far there have only been few attempts at realizing highly configurable hardware emulators Indiveri et al (2006); Vogelstein et al (2007); Rocke et al (2008); Schemmel et al (2010); Furber et al (2012). This approach alone, however, does not completely resolve the computational bottleneck of software simulators, as scaling neuromorphic neural networks up in size becomes non-trivial when considering bandwidth limitations between multiple interconnected hardware devices Costas-Santos et al (2007); Berge and Häfliger (2007); Indiveri (2008); Fieres et al (2008); Serrano-Gotarredona et al (2009).…”

Section: Neuromorphic Hardwarementioning

confidence: 99%

Characterization and Compensation of Network-Level Anomalies in Mixed-Signal Neuromorphic Modeling Platforms

et al. 2014

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Advancing the size and complexity of neural network models leads to an ever increasing demand for computational resources for their simulation. Neuromorphic devices offer a number of advantages over conventional computing architectures, such as high emulation speed or low power consumption, but this usually comes at the price of reduced configurability and precision. In this article, we investigate the consequences of several such factors that are common to neuromorphic devices, more specifically limited hardware resources, limited parameter configurability and parameter variations due to fixed-pattern noise and trial-to-trial variability. Our final aim is to provide an array of methods for coping with such inevitable distortion mechanisms. As a platform for testing our proposed strategies, we use an executable system specification (ESS) of the BrainScaleS neuromorphic system, which has been designed as a universal emulation back-end for neuroscientific modeling. We address the most essential limitations of this device in detail and study their effects on three prototypical benchmark network models within a well-defined, systematic workflow. For each network model, we start by defining quantifiable functionality measures by which we then assess the effects of typical hardware-specific distortion mechanisms, both in idealized software simulations and on the ESS. For those effects that cause unacceptable deviations from the original network dynamics, we suggest generic compensation mechanisms and demonstrate their effectiveness. Both the suggested workflow and the investigated compensation mechanisms are largely back-end independent and do not require additional hardware configurability beyond the one required to emulate the benchmark networks in the first place. We hereby provide a generic methodological environment for configurable neuromorphic devices that are targeted at emulating large-scale, functional neural networks.

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Section: Neuromorphic Hardwarementioning

confidence: 99%

Characterization and Compensation of Network-Level Anomalies in Mixed-Signal Neuromorphic Modeling Platforms

et al. 2014

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“…This research focuses on the evolution of SNN synaptic weights and thresholds. The GA parameters and evolutionary mechanisms employed for this research are as detailed in [35]. A comprehensive review of the application of GAs to ANN training is presented in [30] while GAs have been employed to train SNNs in [31][32][33][34][36][37][38].…”

Section: Genetic Algorithmsmentioning

confidence: 99%

Spiking Neural Networks for Breast Cancer Classification in a Dielectrically Heterogeneous Breast

O’Halloran¹,

McGinley²,

Conceição³

et al. 2011

PIER

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Abstract-The considerable overlap in the dielectric properties of benign and malignant tissue at microwave frequencies means that breast tumour classification using traditional UWB Radar imaging algorithms could be very problematic. Several studies have examined the possibility of using the Radar Target Signature (RTS) of a tumour to classify the tumour as either benign or malignant, since the RTS has been shown to be influenced by the size, shape and surface texture of tumours. The main weakness of existing studies is that they mainly consider tumours in a 3D dielectrically homogenous or 2D heterogeneous breast model. In this paper, the effects of dielectric heterogeneity on a novel Spiking Neural Network (SNN) classifier are examined in terms of both sensitivity and specificity, using a 3D dielectrically heterogeneous breast model. The performance of the SNN classifier is compared to an existing LDA classifier. The effect of combining conflicting classification readings in a multi-antenna system is also considered. Finally and importantly, misclassified tumours are analysed and suggestions for future work are discussed.

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“…In this research, the evolution of SNN synaptic weights and neuron firing thresholds is investigated. The initial population of SNNs is randomly generated by the GA where the properties (synaptic input weights, neuron firing threshold) of each node are encoded in a data string or genome [50]. The resulting genome is used to configure and implement the corresponding SNN.…”

Section: Genetic Algorithmsmentioning

confidence: 99%

Spiking Neural Networks for Breast Cancer Classification Using Radar Target Signatures

McGinley¹,

O’Halloran²,

Conceição³

et al. 2010

PIER C

Self Cite

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Recent studies have shown that the dielectric properties of normal breast tissue vary considerably. This dielectric heterogeneity may mean that the identification of tumours using Ultra Wideband Radar imaging alone may be quite difficult. Significantly, since the dielectric properties of benign tissue were shown to overlap with those of malignant, breast tumour classification using traditional UWB Radar imaging algorithms could be very problematic. Rather than simply examining the dielectric properties of scatterers within the breast, other features of scatterers must be used for classification. Radar Target Signatures have been previously used to classify tumours due to the significant difference in size, shape and surface texture between benign and malignant tumours. This paper investigates Spiking Neural Networks (SNNs) applied as a novel tumour classification method. This paper will describe the creation of 3D tumour models, the generation of representative backscatter, the application of a feature extraction method and the use of SNNs to classify tumours as either benign or malignant. The performance of the SNN classifier is shown to outperform existing UWB Radar classification algorithms.

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Investigating the Suitability of FPAAs for Evolved Hardware Spiking Neural Networks

Cited by 21 publications

References 14 publications

Characterization and Compensation of Network-Level Anomalies in Mixed-Signal Neuromorphic Modeling Platforms

Characterization and Compensation of Network-Level Anomalies in Mixed-Signal Neuromorphic Modeling Platforms

Spiking Neural Networks for Breast Cancer Classification in a Dielectrically Heterogeneous Breast

Spiking Neural Networks for Breast Cancer Classification Using Radar Target Signatures

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