6,287 research outputs found
A Global Model of -Decay Half-Lives Using Neural Networks
Statistical modeling of nuclear data using artificial neural networks (ANNs)
and, more recently, support vector machines (SVMs), is providing novel
approaches to systematics that are complementary to phenomenological and
semi-microscopic theories. We present a global model of -decay
halflives of the class of nuclei that decay 100% by mode in their
ground states. A fully-connected multilayered feed forward network has been
trained using the Levenberg-Marquardt algorithm, Bayesian regularization, and
cross-validation. The halflife estimates generated by the model are discussed
and compared with the available experimental data, with previous results
obtained with neural networks, and with estimates coming from traditional
global nuclear models. Predictions of the new neural-network model are given
for nuclei far from stability, with particular attention to those involved in
r-process nucleosynthesis. This study demonstrates that in the framework of the
-decay problem considered here, global models based on ANNs can at
least match the predictive performance of the best conventional global models
rooted in nuclear theory. Accordingly, such statistical models can provide a
valuable tool for further mapping of the nuclidic chart.Comment: Proceedings of the 16th Panhellenic Symposium of the Hellenic Nuclear
Physics Societ
Significance Driven Hybrid 8T-6T SRAM for Energy-Efficient Synaptic Storage in Artificial Neural Networks
Multilayered artificial neural networks (ANN) have found widespread utility
in classification and recognition applications. The scale and complexity of
such networks together with the inadequacies of general purpose computing
platforms have led to a significant interest in the development of efficient
hardware implementations. In this work, we focus on designing energy efficient
on-chip storage for the synaptic weights. In order to minimize the power
consumption of typical digital CMOS implementations of such large-scale
networks, the digital neurons could be operated reliably at scaled voltages by
reducing the clock frequency. On the contrary, the on-chip synaptic storage
designed using a conventional 6T SRAM is susceptible to bitcell failures at
reduced voltages. However, the intrinsic error resiliency of NNs to small
synaptic weight perturbations enables us to scale the operating voltage of the
6TSRAM. Our analysis on a widely used digit recognition dataset indicates that
the voltage can be scaled by 200mV from the nominal operating voltage (950mV)
for practically no loss (less than 0.5%) in accuracy (22nm predictive
technology). Scaling beyond that causes substantial performance degradation
owing to increased probability of failures in the MSBs of the synaptic weights.
We, therefore propose a significance driven hybrid 8T-6T SRAM, wherein the
sensitive MSBs are stored in 8T bitcells that are robust at scaled voltages due
to decoupled read and write paths. In an effort to further minimize the area
penalty, we present a synaptic-sensitivity driven hybrid memory architecture
consisting of multiple 8T-6T SRAM banks. Our circuit to system-level simulation
framework shows that the proposed synaptic-sensitivity driven architecture
provides a 30.91% reduction in the memory access power with a 10.41% area
overhead, for less than 1% loss in the classification accuracy.Comment: Accepted in Design, Automation and Test in Europe 2016 conference
(DATE-2016
Superpositional Quantum Network Topologies
We introduce superposition-based quantum networks composed of (i) the
classical perceptron model of multilayered, feedforward neural networks and
(ii) the algebraic model of evolving reticular quantum structures as described
in quantum gravity. The main feature of this model is moving from particular
neural topologies to a quantum metastructure which embodies many differing
topological patterns. Using quantum parallelism, training is possible on
superpositions of different network topologies. As a result, not only classical
transition functions, but also topology becomes a subject of training. The main
feature of our model is that particular neural networks, with different
topologies, are quantum states. We consider high-dimensional dissipative
quantum structures as candidates for implementation of the model.Comment: 10 pages, LaTeX2
Adaptive optical networks using photorefractive crystals
The capabilities of photorefractive crystals as media for holographic interconnections in neural networks are examined. Limitations on the density of interconnections and the number of holographic associations which can be stored in photorefractive crystals are derived. Optical architectures for implementing various neural schemes are described. Experimental results are presented for one of these architectures
Hierarchically Clustered Adaptive Quantization CMAC and Its Learning Convergence
No abstract availabl
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