A spiking recurrent neural network with phase change memory neurons and synapses for the accelerated solution of constraint satisfaction problems

Pedretti G, Mannocci P, Hashemkhani S, Milo V, Melnic O, Chicca E, Ielmini D (2020)
IEEE Journal on Exploratory Solid-State Computational Devices and Circuits 6(1): 189-97.

Zeitschriftenaufsatz | Veröffentlicht | Englisch
 
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Autor*in
Pedretti, Giacomo; Mannocci, Piergiulio; Hashemkhani, Shahin; Milo, Valerio; Melnic, Octavian; Chicca, ElisabettaUniBi ; Ielmini, Daniele
Abstract / Bemerkung
Data-intensive computing applications, such as object recognition, time series prediction, and optimization tasks, are becoming increasingly important in several fields, including smart mobility, health, and industry. Because of the large amount of data involved in the computation, the conventional von Neumann architecture suffers from excessive latency and energy consumption due to the memory bottleneck. A more efficient approach consists of in-memory computing (IMC), where computational operations are directly carried out within the data. IMC can take advantage of the rich physics of memory devices, such as their ability to store analog values to be used in matrix-vector multiplication (MVM) and their stochasticity that is highly valuable in the frame of optimization and constraint satisfaction problems (CSPs). This article presents a stochastic spiking neuron based on a phase-change memory (PCM) device for the solution of CSPs within a Hopfield recurrent neural network (RNN). In the RNN, the PCM cell is used as the integrating element of a stochastic neuron, supporting the solution of a typical CSP, namely a Sudoku puzzle in hardware. Finally, the ability to solve Sudoku puzzles using RNNs with PCM-based neurons is studied for increasing size of Sudoku puzzles by a compact simulation model, thus supporting our PCM-based RNN for data-intensive computing.
Erscheinungsjahr
2020
Zeitschriftentitel
IEEE Journal on Exploratory Solid-State Computational Devices and Circuits
Band
6
Ausgabe
1
Seite(n)
189-97
eISSN
2329-9231
Page URI
https://pub.uni-bielefeld.de/record/2943346

Zitieren

Pedretti G, Mannocci P, Hashemkhani S, et al. A spiking recurrent neural network with phase change memory neurons and synapses for the accelerated solution of constraint satisfaction problems. IEEE Journal on Exploratory Solid-State Computational Devices and Circuits. 2020;6(1):189-97.
Pedretti, G., Mannocci, P., Hashemkhani, S., Milo, V., Melnic, O., Chicca, E., & Ielmini, D. (2020). A spiking recurrent neural network with phase change memory neurons and synapses for the accelerated solution of constraint satisfaction problems. IEEE Journal on Exploratory Solid-State Computational Devices and Circuits, 6(1), 189-97. doi:10.1109/jxcdc.2020.2992691
Pedretti, G., Mannocci, P., Hashemkhani, S., Milo, V., Melnic, O., Chicca, E., and Ielmini, D. (2020). A spiking recurrent neural network with phase change memory neurons and synapses for the accelerated solution of constraint satisfaction problems. IEEE Journal on Exploratory Solid-State Computational Devices and Circuits 6, 189-97.
Pedretti, G., et al., 2020. A spiking recurrent neural network with phase change memory neurons and synapses for the accelerated solution of constraint satisfaction problems. IEEE Journal on Exploratory Solid-State Computational Devices and Circuits, 6(1), p 189-97.
G. Pedretti, et al., “A spiking recurrent neural network with phase change memory neurons and synapses for the accelerated solution of constraint satisfaction problems”, IEEE Journal on Exploratory Solid-State Computational Devices and Circuits, vol. 6, 2020, pp. 189-97.
Pedretti, G., Mannocci, P., Hashemkhani, S., Milo, V., Melnic, O., Chicca, E., Ielmini, D.: A spiking recurrent neural network with phase change memory neurons and synapses for the accelerated solution of constraint satisfaction problems. IEEE Journal on Exploratory Solid-State Computational Devices and Circuits. 6, 189-97 (2020).
Pedretti, Giacomo, Mannocci, Piergiulio, Hashemkhani, Shahin, Milo, Valerio, Melnic, Octavian, Chicca, Elisabetta, and Ielmini, Daniele. “A spiking recurrent neural network with phase change memory neurons and synapses for the accelerated solution of constraint satisfaction problems”. IEEE Journal on Exploratory Solid-State Computational Devices and Circuits 6.1 (2020): 189-97.

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