Reservoir computing with a single delay-coupled non-linear mechanical oscillator

Reservoir computing was achieved by constructing a network of virtual nodes multiplexed in time and sharing a single silicon beam exhibiting a classical Duffing non-linearity as the source of nonlinearity. The delay-coupled electromechanical system performed well on time series classification tasks, with error rates below 0.1% for the 1st, 2nd, and 3rd order parity benchmarks and an accuracy of ( 78 ± 2 ) % for the TI-46 spoken word recognition benchmark. As a first demonstration of reservoir computing using a non-linear mass-spring system in MEMS, this result paves the way to the creation of a new class of compact devices combining the functions of sensing and computing.Reservoir computing was achieved by constructing a network of virtual nodes multiplexed in time and sharing a single silicon beam exhibiting a classical Duffing non-linearity as the source of nonlinearity. The delay-coupled electromechanical system performed well on time series classification tasks, with error rates below 0.1% for the 1st, 2nd, and 3rd order parity benchmarks and an accuracy of ( 78 ± 2 ) % for the TI-46 spoken word recognition benchmark. As a first demonstration of reservoir computing using a non-linear mass-spring system in MEMS, this result paves the way to the creation of a new class of compact devices combining the functions of sensing and computing.

[1]  Robert A. Legenstein,et al.  2007 Special Issue: Edge of chaos and prediction of computational performance for neural circuit models , 2007 .

[2]  Miguel C. Soriano,et al.  Digital Implementation of a Single Dynamical Node Reservoir Computer , 2015, IEEE Transactions on Circuits and Systems II: Express Briefs.

[3]  Daniel Brunner,et al.  Parallel photonic information processing at gigabyte per second data rates using transient states , 2013, Nature Communications.

[4]  Dhireesha Kudithipudi,et al.  Memristive Reservoir Computing Architecture for Epileptic Seizure Detection , 2014, BICA.

[5]  Jan Danckaert,et al.  Delay-Based Reservoir Computing: Noise Effects in a Combined Analog and Digital Implementation , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[6]  Herbert Jaeger,et al.  Reservoir computing approaches to recurrent neural network training , 2009, Comput. Sci. Rev..

[7]  Leon O. Chua,et al.  Fading memory and the problem of approximating nonlinear operators with volterra series , 1985 .

[8]  Harald Haas,et al.  Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication , 2004, Science.

[9]  Laurent Larger,et al.  High-Speed Photonic Reservoir Computing Using a Time-Delay-Based Architecture: Million Words per Second Classification , 2017 .

[10]  Jan Danckaert,et al.  Constructing optimized binary masks for reservoir computing with delay systems , 2014, Scientific Reports.

[11]  Serge Massar,et al.  All-optical Reservoir Computing , 2012, Optics express.

[12]  Nils Bertschinger,et al.  Real-Time Computation at the Edge of Chaos in Recurrent Neural Networks , 2004, Neural Computation.

[13]  Julien Sylvestre,et al.  Computing with networks of nonlinear mechanical oscillators , 2017, PloS one.

[14]  Seung Hwan Lee,et al.  Reservoir computing using dynamic memristors for temporal information processing , 2017, Nature Communications.

[15]  Benjamin Schrauwen,et al.  Real-time detection of epileptic seizures in animal models using reservoir computing , 2013, Epilepsy Research.

[16]  Damien Querlioz,et al.  Neuromorphic computing with nanoscale spintronic oscillators , 2017, Nature.

[17]  Lennert Appeltant Reservoir computing based on delay-dynamical systems , 2012 .

[19]  Yuichi Nakamura,et al.  Approximation of dynamical systems by continuous time recurrent neural networks , 1993, Neural Networks.

[20]  M. C. Soriano,et al.  A Unified Framework for Reservoir Computing and Extreme Learning Machines based on a Single Time-delayed Neuron , 2015, Scientific Reports.

[21]  Cory Merkel,et al.  Design and Analysis of a Neuromemristive Reservoir Computing Architecture for Biosignal Processing , 2016, Front. Neurosci..

[22]  L Pesquera,et al.  Photonic information processing beyond Turing: an optoelectronic implementation of reservoir computing. , 2012, Optics express.

[23]  Geert Morthier,et al.  Experimental demonstration of reservoir computing on a silicon photonics chip , 2014, Nature Communications.

[24]  Serge Massar,et al.  Fully analogue photonic reservoir computer , 2016, Scientific Reports.

[25]  Benjamin Schrauwen,et al.  Locomotion Without a Brain: Physical Reservoir Computing in Tensegrity Structures , 2013, Artificial Life.

[26]  L. Appeltant,et al.  Information processing using a single dynamical node as complex system , 2011, Nature communications.

[27]  Benjamin Schrauwen,et al.  Optoelectronic Reservoir Computing , 2011, Scientific Reports.

[28]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[29]  Yoshua Bengio,et al.  Learning long-term dependencies with gradient descent is difficult , 1994, IEEE Trans. Neural Networks.

[30]  Bin Tang,et al.  Using nonlinear jumps to estimate cubic stiffness nonlinearity: An experimental study , 2016 .

[31]  Dan Wang,et al.  Prediction performance of reservoir computing system based on a semiconductor laser subject to double optical feedback and optical injection. , 2018, Optics express.