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Energy scaling advantages of resistive memory crossbar based computation and its application to sparse coding

Frontiers in Neuroscience

Agarwal, Sapan A.; Quach, Tu-Thach Q.; Parekh, Ojas D.; Hsia, Alexander H.; DeBenedictis, Erik; James, Conrad D.; Marinella, Matthew J.; Aimone, James B.

The exponential increase in data over the last decade presents a significant challenge to analytics efforts that seek to process and interpret such data for various applications. Neural-inspired computing approaches are being developed in order to leverage the computational properties of the analog, low-power data processing observed in biological systems. Analog resistive memory crossbars can perform a parallel read or a vector-matrix multiplication as well as a parallel write or a rank-1 update with high computational efficiency. For an N × N crossbar, these two kernels can be O(N) more energy efficient than a conventional digital memory-based architecture. If the read operation is noise limited, the energy to read a column can be independent of the crossbar size (O(1)). These two kernels form the basis of many neuromorphic algorithms such as image, text, and speech recognition. For instance, these kernels can be applied to a neural sparse coding algorithm to give an O(N) reduction in energy for the entire algorithm when run with finite precision. Sparse coding is a rich problem with a host of applications including computer vision, object tracking, and more generally unsupervised learning.

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TYPE Journal Article YEAR 2016
ScopusOSTIDOI

The energy scaling advantages of RRAM crossbars

2015 4th Berkeley Symposium on Energy Efficient Electronic Systems, E3S 2015 - Proceedings

Agarwal, Sapan A.; Parekh, Ojas D.; Quach, Tu-Thach Q.; James, Conrad D.; Aimone, James B.; Marinella, Matthew J.

As transistors start to approach fundamental limits and Moore's law slows down, new devices and architectures are needed to enable continued performance gains. New approaches based on RRAM (resistive random access memory) or memristor crossbars can enable the processing of large amounts of data[1, 2]. One of the most promising applications for RRAM crossbars is brain inspired or neuromorphic computing[3, 4].

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TYPE Conference Poster YEAR 2015
ScopusOSTI

Repeated play of the SVM game as a means of adaptive classification

Proceedings of the International Joint Conference on Neural Networks

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

The field of machine learning strives to develop algorithms that, through learning, lead to generalization; that is, the ability of a machine to perform a task that it was not explicitly trained for. An added challenge arises when the problem domain is dynamic or non-stationary with the data distributions or categorizations changing over time. This phenomenon is known as concept drift. Game-theoretic algorithms are often iterative by nature, consisting of repeated game play rather than a single interaction. Effectively, rather than requiring extensive retraining to update a learning model, a game-theoretic approach can adjust strategies as a novel approach to concept drift. In this paper we present a variant of our Support Vector Machine (SVM) Game classifier which may be used in an adaptive manner with repeated play to address concept drift, and show results of applying this algorithm to synthetic as well as real data.

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TYPE Conference Poster YEAR 2015
ScopusOSTI

Training neural hardware with noisy components

Proceedings of the International Joint Conference on Neural Networks

Rothganger, Fredrick R.; Evans, Brian R.; Aimone, James B.; DeBenedictis, Erik

Some next generation computing devices may consist of resistive memory arranged as a crossbar. Currently, the dominant approach is to use crossbars as the weight matrix of a neural network, and to use learning algorithms that require small incremental weight updates, such as gradient descent (for example Backpropagation). Using real-world measurements, we demonstrate that resistive memory devices are unlikely to support such learning methods. As an alternative, we offer a random search algorithm tailored to the measured characteristics of our devices.

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TYPE Conference Poster YEAR 2015
ScopusOSTI

Using High Performance Computing to Examine the Processes of Neurogenesis Underlying Pattern Separation/Completion of Episodic Information

Aimone, James B.; Betty, Rita B.

Using High Performance Computing to Examine the Processes of Neurogenesis Underlying Pattern Separation/Completion of Episodic Information - Sandia researchers developed novel methods and metrics for studying the computational function of neurogenesis, thus generating substantial impact to the neuroscience and neural computing communities. This work could benefit applications in machine learning and other analysis activities.

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TYPE Other Report YEAR 2015
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Results 126–150 of 184
Results 126–150 of 184