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Mixing wave-based computing

a technology of mixing wave and computing, applied in the field of optical wave-based computing, can solve the problems of limiting the complexity of tasks that can be performed, limiting the complexity of proposed implementation, and most of this processing still happens, so as to improve the signal mixing properties of the mixing unit, reduce the complexity of the overall system, and improve the effect of memory

Inactive Publication Date: 2020-07-16
UNIV GENT +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Benefits of technology

The system achieves low-loss wave-based computing with improved mixing efficiency, enabling the processing of more complex tasks by maintaining a high Q-factor and extending the memory of the system, thus overcoming the limitations of traditional reservoir computing.

Problems solved by technology

However, most of this processing still happens in software on power-hungry computers.
However, the proposed implementation suffers from fundamental scaling limits coming from the 3 dB loss associated with the sequence of combiners used in the system.
Nevertheless, the complexity of tasks the reservoir can perform depends on the number of neurons it consists of, these losses also limit the complexity of tasks that can be performed, and consequently there is still room for improvement.

Method used

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Examples

Experimental program
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example 1

[0086]In the first example, benchmarking of the wave-based computing system as shown in FIG. 1 is discussed.

[0087]A first benchmark for the memory of the signal is the ability to reproduce the exact input with a certain amount of delay. From FIG. 7, for the current design, there is quite a wide basin of operation. Using 5 outputs results in being able to reproduce the input with a larger delay, which makes sense because of the increased Q factor and thus memory for 5 outputs.

[0088]A second benchmark which illustrates the ability to perform Boolean operations is the nonlinear xor task, where the xor is taken between two bits bn and bn-k, k bits apart. Since a normal conventional linear classifier can only achieve a minimum of 25% error rate, it is also a good performance indicator of the nonlinearity in the system.

[0089]First, the performance of an xor on two neighboring bits was assessed, while continuously increasing the bit separation. This is called the xor-specific memory of the...

example 2

[0094]By way of illustration, embodiments of the present invention not being limited thereto, a second example is further discussed of use of a photonic crystal cavity with a quarter-stadium shape, which fosters interesting mixing dynamics. These mixing properties turn out to be very useful for memory-dependent optical signal processing tasks, such as header recognition. The proposed, ultra-compact photonic crystal cavity exhibits a memory of up to 6 bits, while simultaneously accepting bitrates in a wide region of operation. Moreover, because of the inherent low losses in a high-Q photonic crystal cavity, the proposed design is very power efficient.

[0095]The system consists of an on-chip photonic crystal cavity in the shape of a quarter-stadium resonator, which fosters interesting mixing of the fields in an almost chaotic manner. The photonic crystal cavity was designed for the 220 nm silicon photonics platform, consisting of holes etched from a 220 nm silicon slab with radius r=0....

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Abstract

A passive photonics wave-based computing system configured for performing wave-based computing, the system comprising a mixing unit comprising a plurality of inputs and a plurality of outputs the mixing unit being a cavity and further being configured for reflecting or scattering, on average at least three times in the cavity, an optical beam received via said at least one input towards at least one of said plurality of outputs.

Description

FIELD OF THE INVENTION[0001]The invention relates to the field of optical wave-based computing. More specifically it relates to methods and systems for optical wave-based computing with low loss using a mixing unit.BACKGROUND OF THE INVENTION[0002]The more we get swamped by Big Data, the more we let computers take over the data processing. Machine Learning is therefore currently one of the fastest growing disciplines in computer science and statistics. However, most of this processing still happens in software on power-hungry computers.[0003]An alternative hardware implementation is a so-called Reservoir Computer specifically designed for efficient optical computation. Reservoir Computing is a machine learning branch focusing on processing of time-dependent data. It was first proposed in the early 2000s as a way of using an untrained neural network with internal feedback combined with a trained linear readout layer to perform classification of temporal data. The feedback through the...

Claims

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Application Information

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Patent Type & Authority Applications(United States)
IPC IPC(8): G01N21/47G06K9/00G06K9/20G02B6/122G06N3/067H04N25/00
CPCG06K9/2036G02B6/1228G01N2201/10G01N2201/06113G01N21/47G02B6/1225G06N3/0675G06K9/00134G06K9/00147G01N33/48G06V20/698G06V20/693H04N25/00
Inventor BIENSTMAN, PETERLAPORTE, FLORISLUGNAN, ALESSIO
Owner UNIV GENT
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