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Spiking network algorithms for scientific computing

2016 IEEE International Conference on Rebooting Computing, ICRC 2016 - Conference Proceedings

Severa, William M.; Parekh, Ojas D.; Carlson, Kristofor D.; James, Conrad D.; Aimone, James B.

For decades, neural networks have shown promise for next-generation computing, and recent breakthroughs in machine learning techniques, such as deep neural networks, have provided state-of-the-art solutions for inference problems. However, these networks require thousands of training processes and are poorly suited for the precise computations required in scientific or similar arenas. The emergence of dedicated spiking neuromorphic hardware creates a powerful computational paradigm which can be leveraged towards these exact scientific or otherwise objective computing tasks. We forego any learning process and instead construct the network graph by hand. In turn, the networks produce guaranteed success often with easily computable complexity. We demonstrate a number of algorithms exemplifying concepts central to spiking networks including spike timing and synaptic delay. We also discuss the application of cross-correlation particle image velocimetry and provide two spiking algorithms; one uses time-division multiplexing, and the other runs in constant time.

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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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Quantum Graph Analysis

Maunz, Peter L.; Sterk, Jonathan D.; Lobser, Daniel L.; Parekh, Ojas D.; Ryan-Anderson, Ciaran R.

In recent years, advanced network analytics have become increasingly important to na- tional security with applications ranging from cyber security to detection and disruption of ter- rorist networks. While classical computing solutions have received considerable investment, the development of quantum algorithms to address problems, such as data mining of attributed relational graphs, is a largely unexplored space. Recent theoretical work has shown that quan- tum algorithms for graph analysis can be more efficient than their classical counterparts. Here, we have implemented a trapped-ion-based two-qubit quantum information proces- sor to address these goals. Building on Sandia's microfabricated silicon surface ion traps, we have designed, realized and characterized a quantum information processor using the hyperfine qubits encoded in two 171 Yb + ions. We have implemented single qubit gates using resonant microwave radiation and have employed Gate set tomography (GST) to characterize the quan- tum process. For the first time, we were able to prove that the quantum process surpasses the fault tolerance thresholds of some quantum codes by demonstrating a diamond norm distance of less than 1 . 9 x 10 [?] 4 . We used Raman transitions in order to manipulate the trapped ions' motion and realize two-qubit gates. We characterized the implemented motion sensitive and insensitive single qubit processes and achieved a maximal process infidelity of 6 . 5 x 10 [?] 5 . We implemented the two-qubit gate proposed by Molmer and Sorensen and achieved a fidelity of more than 97 . 7%.

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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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Benchmarking Adiabatic Quantum Optimization for Complex Network Analysis

Parekh, Ojas D.; Wendt, Jeremy D.; Shulenburger, Luke N.; Landahl, Andrew J.; Moussa, Jonathan E.; Aidun, John B.

We lay the foundation for a benchmarking methodology for assessing current and future quantum computers. We pose and begin addressing fundamental questions about how to fairly compare computational devices at vastly different stages of technological maturity. We critically evaluate and offer our own contributions to current quantum benchmarking efforts, in particular those involving adiabatic quantum computation and the Adiabatic Quantum Optimizers produced by D-Wave Systems, Inc. We find that the performance of D-Wave's Adiabatic Quantum Optimizers scales roughly on par with classical approaches for some hard combinatorial optimization problems; however, architectural limitations of D-Wave devices present a significant hurdle in evaluating real-world applications. In addition to identifying and isolating such limitations, we develop algorithmic tools for circumventing these limitations on future D-Wave devices, assuming they continue to grow and mature at an exponential rate for the next several years.

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On Bipartite Graphs Trees and Their Partial Vertex Covers

Caskurlu, Bugra C.; Mkrtchyan, Vahan M.; Parekh, Ojas D.; Subramani, K.S.

Graphs can be used to model risk management in various systems. Particularly, Caskurlu et al. in [7] have considered a system, which has threats, vulnerabilities and assets, and which essentially represents a tripartite graph. The goal in this model is to reduce the risk in the system below a predefined risk threshold level. One can either restricting the permissions of the users, or encapsulating the system assets. The pointed out two strategies correspond to deleting minimum number of elements corresponding to vulnerabilities and assets, such that the flow between threats and assets is reduced below the predefined threshold level. It can be shown that the main goal in this risk management system can be formulated as a Partial Vertex Cover problem on bipartite graphs. It is well-known that the Vertex Cover problem is in P on bipartite graphs, however; the computational complexity of the Partial Vertex Cover problem on bipartite graphs has remained open. In this paper, we establish that the Partial Vertex Cover problem is NP-hard on bipartite graphs, which was also recently independently demonstrated [N. Apollonio and B. Simeone, Discrete Appl. Math., 165 (2014), pp. 37–48; G. Joret and A. Vetta, preprint, arXiv:1211.4853v1 [cs.DS], 2012]. We then identify interesting special cases of bipartite graphs, for which the Partial Vertex Cover problem, the closely related Budgeted Maximum Coverage problem, and their weighted extensions can be solved in polynomial time. We also present an 8/9-approximation algorithm for the Budgeted Maximum Coverage problem in the class of bipartite graphs. We show that this matches and resolves the integrality gap of the natural LP relaxation of the problem and improves upon a recent 4/5-approximation.

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Results 51–75 of 98
Results 51–75 of 98