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Resistive memory device requirements for a neural algorithm accelerator

Proceedings of the International Joint Conference on Neural Networks

Agarwal, Sapan; Plimpton, Steven J.; Hughart, David R.; Hsia, Alexander W.; Richter, Isaac; Cox, Jonathan A.; James, Conrad D.; Marinella, Matthew

Resistive memories enable dramatic energy reductions for neural algorithms. We propose a general purpose neural architecture that can accelerate many different algorithms and determine the device properties that will be needed to run backpropagation on the neural architecture. To maintain high accuracy, the read noise standard deviation should be less than 5% of the weight range. The write noise standard deviation should be less than 0.4% of the weight range and up to 300% of a characteristic update (for the datasets tested). Asymmetric nonlinearities in the change in conductance vs pulse cause weight decay and significantly reduce the accuracy, while moderate symmetric nonlinearities do not have an effect. In order to allow for parallel reads and writes the write current should be less than 100 nA as well.

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TYPE Conference Poster YEAR 2016
DOIOSTIScopus

Quantifying neural information content: A case study of the impact of hippocampal adult neurogenesis

Proceedings of the International Joint Conference on Neural Networks

Vineyard, Craig M.; Verzi, Stephen J.; James, Conrad D.; Aimone, James B.

Through various means of structural and synaptic plasticity enabling online learning, neural networks are constantly reconfiguring their computational functionality. Neural information content is embodied within the configurations, representations, and computations of neural networks. To explore neural information content, we have developed metrics and computational paradigms to quantify neural information content. We have observed that conventional compression methods may help overcome some of the limiting factors of standard information theoretic techniques employed in neuroscience, and allows us to approximate information in neural data. To do so we have used compressibility as a measure of complexity in order to estimate entropy to quantitatively assess information content of neural ensembles. Using Lempel-Ziv compression we are able to assess the rate of generation of new patterns across a neural ensemble's firing activity over time to approximate the information content encoded by a neural circuit. As a specific case study, we have been investigating the effect of neural mixed coding schemes due to hippocampal adult neurogenesis.

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TYPE Other Report YEAR 2016
DOIOSTIScopus
Results 4426–4450 of 9,998
Results 4426–4450 of 9,998
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