Compact hardware liquid state machines on FPGA for real-time speech recognition

Schrauwen, Benjamin; D’Haene, Michiel; Verstraeten, David; Campenhout, J. Van

doi:10.1016/j.neunet.2007.12.009

Cited by 117 publications

(52 citation statements)

References 28 publications

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“…In previous work, RC has already proven its capabilities in a broad range of applications including robot localization [10], chaotic time series prediction [11] and speech recognition [12]. Additionally, researchers are making efforts to directly implement such systems on hardware [13].…”

Section: Reservoir Computingmentioning

confidence: 99%

Towards a Neural Hierarchy of Time Scales for Motor Control

Waegeman

wyffels

Schrauwen

2012

From Animals to Animats 12

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Abstract. Animals show remarkable rich motion skills which are still far from realizable with robots. Inspired by the neural circuits which generate rhythmic motion patterns in the spinal cord of all vertebrates, one main research direction points towards the use of central pattern generators in robots. On of the key advantages of this, is that the dimensionality of the control problem is reduced. In this work we investigate this further by introducing a multi-timescale control hierarchy with at its core a hierarchy of recurrent neural networks. By means of some robot experiments, we demonstrate that this hierarchy can embed any rhythmic motor signal by imitation learning. Furthermore, the proposed hierarchy allows the tracking of several high level motion properties (e.g.: amplitude and offset), which are usually observed at a slower rate than the generated motion. Although these experiments are preliminary, the results are promising and have the potential to open the door for rich motor skills and advanced control.

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Section: Reservoir Computingmentioning

confidence: 99%

Towards a Neural Hierarchy of Time Scales for Motor Control

Waegeman

wyffels

Schrauwen

2012

From Animals to Animats 12

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“…Field Programmable Gate Arrays (FPGAs) are particularly suited for such applications. Several RC implementations using an FPGA chip have been reported [2,8,17], but no interconnection between an FPGA and a physical reservoir computer has been reported so far.…”

Section: Introductionmentioning

confidence: 99%

Online Training of an Opto-Electronic Reservoir Computer

Antonik

Duport

Smerieri

et al. 2015

Neural Information Processing

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Abstract. Reservoir Computing is a bio-inspired computing paradigm for processing time dependent signals. Its analog implementations equal and sometimes outperform other digital algorithms on a series of benchmark tasks. Their performance can be increased by switching from offline to online training method. Here we present the first online trained opto-electronic reservoir computer. The system is tested on a channel equalisation task and the algorithm is executed by an FPGA chip. We report performances close to previous implementations and demonstrate the benefits of online training on a non-stationary task that could not be easily solved using offline methods.

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“…Using fundamentally different computational principles, ANNs can outperform conventional computer architectures in a variety of tasks, such as autonomous signal generation, control functions in robotics, chaotic time series prediction and speech recognition [1][2][3]. Spiking Neural Networks (SSNs) have become increasingly popular during the last decade [4,5].…”

Section: Introductionmentioning

confidence: 99%

“…Functional SNNs have recently been realized in electronics [3,7]. Notwithstanding the potential of these electronic systems, they are subject to a fundamental bandwidth fan-in product limit.…”

Section: Introductionmentioning

confidence: 99%

Excitability in optically injected microdisk lasers with phase controlled excitatory and inhibitory response

Alexander¹,

Vaerenbergh²,

Fiers³

et al. 2013

Opt. Express

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Abstract:We demonstrate class I excitability in optically injected microdisk lasers, and propose a possible optical spiking neuron design. The neuron has a clear threshold and an integrating behavior, leading to an output rate-input rate dependency that is comparable to the characteristic of sigmoidal artificial neurons. We also show that the optical phase of the input pulses has influence on the neuron response, and can be used to create inhibitory, as well as excitatory perturbations.

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Compact hardware liquid state machines on FPGA for real-time speech recognition

Cited by 117 publications

References 28 publications

Towards a Neural Hierarchy of Time Scales for Motor Control

Towards a Neural Hierarchy of Time Scales for Motor Control

Online Training of an Opto-Electronic Reservoir Computer

Excitability in optically injected microdisk lasers with phase controlled excitatory and inhibitory response

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