Optical neural network

Last updated
Optical logic gate with diffractive neural network.png
Microwave diffractive ANN.png
Schematic of an optical neural network that functions as a logic gate (above) and its implementation in microwave frequencies (below). The intermediate diffractive metasurfaces function as hidden layers. [1]

An optical neural network is a physical implementation of an artificial neural network with optical components. Early optical neural networks used a photorefractive Volume hologram to interconnect arrays of input neurons to arrays of output with synaptic weights in proportion to the multiplexed hologram's strength. [2] Volume holograms were further multiplexed using spectral hole burning to add one dimension of wavelength to space to achieve four dimensional interconnects of two dimensional arrays of neural inputs and outputs. [3] This research led to extensive research on alternative methods using the strength of the optical interconnect for implementing neuronal communications. [4]

Contents

Some artificial neural networks that have been implemented as optical neural networks include the Hopfield neural network [5] and the Kohonen self-organizing map with liquid crystal spatial light modulators [6] Optical neural networks can also be based on the principles of neuromorphic engineering, creating neuromorphic photonic systems. Typically, these systems encode information in the networks using spikes, mimicking the functionality of spiking neural networks in optical and photonic hardware. Photonic devices that have demonstrated neuromorphic functionalities include (among others) vertical-cavity surface-emitting lasers, [7] [8] integrated photonic modulators, [9] optoelectronic systems based on superconducting Josephson junctions [10] or systems based on resonant tunnelling diodes. [11]

Electrochemical vs. optical neural networks

Biological neural networks function on an electrochemical basis, while optical neural networks use electromagnetic waves. Optical interfaces to biological neural networks can be created with optogenetics, but is not the same as an optical neural networks. In biological neural networks there exist a lot of different mechanisms for dynamically changing the state of the neurons, these include short-term and long-term synaptic plasticity. Synaptic plasticity is among the electrophysiological phenomena used to control the efficiency of synaptic transmission, long-term for learning and memory, and short-term for short transient changes in synaptic transmission efficiency. Implementing this with optical components is difficult, and ideally requires advanced photonic materials. Properties that might be desirable in photonic materials for optical neural networks include the ability to change their efficiency of transmitting light, based on the intensity of incoming light.

Rising Era of Optical Neural Networks

With the increasing significance of computer vision in various domains, the computational cost of these tasks has increased, making it more important to develop the new approaches of the processing acceleration. Optical computing has emerged as a potential alternative to GPU acceleration for modern neural networks, particularly considering the looming obsolescence of Moore's Law. Consequently, optical neural networks have garnered increased attention in the research community. Presently, two primary methods of optical neural computing are under research: silicon photonics-based and free-space optics. Each approach has its benefits and drawbacks; while silicon photonics may offer superior speed, it lacks the massive parallelism that free-space optics can deliver. Given the substantial parallelism capabilities of free-space optics, researchers have focused on taking advantage of it. One implementation, proposed by Lin et al., [12] involves the training and fabrication of phase masks for a handwritten digit classifier. By stacking 3D-printed phase masks, light passing through the fabricated network can be read by a photodetector array of ten detectors, each representing a digit class ranging from 1 to 10. Although this network can achieve terahertz-range classification, it lacks flexibility, as the phase masks are fabricated for a specific task and cannot be retrained. An alternative method for classification in free-space optics, introduced by Cahng et al., [13] employs a 4F system that is based on the convolution theorem to perform convolution operations. This system uses two lenses to execute the Fourier transforms of the convolution operation, enabling passive conversion into the Fourier domain without power consumption or latency. However, the convolution operation kernels in this implementation are also fabricated phase masks, limiting the device's functionality to specific convolutional layers of the network only. In contrast, Li et al. [14] proposed a technique involving kernel tiling to use the parallelism of the 4F system while using a Digital Micromirror Device (DMD) instead of a phase mask. This approach allows users to upload various kernels into the 4F system and execute the entire network's inference on a single device. Unfortunately, modern neural networks are not designed for the 4F systems, as they were primarily developed during the CPU/GPU era. Mostly because they tend to use a lower resolution and a high number of channels in their feature maps.

Other Implementations

In 2007 there was one model of Optical Neural Network: the Programmable Optical Array/Analogic Computer (POAC). It had been implemented in the year 2000 and reported based on modified Joint Fourier Transform Correlator (JTC) and Bacteriorhodopsin (BR) as a holographic optical memory. Full parallelism, large array size and the speed of light are three promises offered by POAC to implement an optical CNN. They had been investigated during the last years with their practical limitations and considerations yielding the design of the first portable POAC version.

The practical details – hardware (optical setups) and software (optical templates) – were published. However, POAC is a general purpose and programmable array computer that has a wide range of applications including:

See also

Related Research Articles

<span class="mw-page-title-main">Photonic crystal</span> Periodic optical nanostructure that affects the motion of photons

A photonic crystal is an optical nanostructure in which the refractive index changes periodically. This affects the propagation of light in the same way that the structure of natural crystals gives rise to X-ray diffraction and that the atomic lattices of semiconductors affect their conductivity of electrons. Photonic crystals occur in nature in the form of structural coloration and animal reflectors, and, as artificially produced, promise to be useful in a range of applications.

Neuromorphic computing is an approach to computing that is inspired by the structure and function of the human brain. A neuromorphic computer/chip is any device that uses physical artificial neurons to do computations. In recent times, the term neuromorphic has been used to describe analog, digital, mixed-mode analog/digital VLSI, and software systems that implement models of neural systems. The implementation of neuromorphic computing on the hardware level can be realized by oxide-based memristors, spintronic memories, threshold switches, transistors, among others. Training software-based neuromorphic systems of spiking neural networks can be achieved using error backpropagation, e.g., using Python based frameworks such as snnTorch, or using canonical learning rules from the biological learning literature, e.g., using BindsNet.

Holonomic brain theory is a branch of neuroscience investigating the idea that human consciousness is formed by quantum effects in or between brain cells. Holonomic refers to representations in a Hilbert phase space defined by both spectral and space-time coordinates. Holonomic brain theory is opposed by traditional neuroscience, which investigates the brain's behavior by looking at patterns of neurons and the surrounding chemistry.

Optical computing or photonic computing uses light waves produced by lasers or incoherent sources for data processing, data storage or data communication for computing. For decades, photons have shown promise to enable a higher bandwidth than the electrons used in conventional computers.

Digital holography refers to the acquisition and processing of holograms with a digital sensor array, typically a CCD camera or a similar device. Image rendering, or reconstruction of object data is performed numerically from digitized interferograms. Digital holography offers a means of measuring optical phase data and typically delivers three-dimensional surface or optical thickness images. Several recording and processing schemes have been developed to assess optical wave characteristics such as amplitude, phase, and polarization state, which make digital holography a very powerful method for metrology applications .

Phased-array optics is the technology of controlling the phase and amplitude of light waves transmitting, reflecting, or captured (received) by a two-dimensional surface using adjustable surface elements. An optical phased array (OPA) is the optical analog of a radio-wave phased array. By dynamically controlling the optical properties of a surface on a microscopic scale, it is possible to steer the direction of light beams, or the view direction of sensors, without any moving parts. Phased-array beam steering is used for optical switching and multiplexing in optoelectronic devices and for aiming laser beams on a macroscopic scale.

Computer-generated holography (CGH) is the method of digitally generating holographic interference patterns. A holographic image can be generated e.g., by digitally computing a holographic interference pattern and printing it onto a mask or film for subsequent illumination by suitable coherent light source.

Reservoir computing is a framework for computation derived from recurrent neural network theory that maps input signals into higher dimensional computational spaces through the dynamics of a fixed, non-linear system called a reservoir. After the input signal is fed into the reservoir, which is treated as a "black box," a simple readout mechanism is trained to read the state of the reservoir and map it to the desired output. The first key benefit of this framework is that training is performed only at the readout stage, as the reservoir dynamics are fixed. The second is that the computational power of naturally available systems, both classical and quantum mechanical, can be used to reduce the effective computational cost.

<span class="mw-page-title-main">Microlens</span> Small lens, generally with a diameter less than a millimetre

A microlens is a small lens, generally with a diameter less than a millimetre (mm) and often as small as 10 micrometres (µm). The small sizes of the lenses means that a simple design can give good optical quality but sometimes unwanted effects arise due to optical diffraction at the small features. A typical microlens may be a single element with one plane surface and one spherical convex surface to refract the light. Because micro-lenses are so small, the substrate that supports them is usually thicker than the lens and this has to be taken into account in the design. More sophisticated lenses may use aspherical surfaces and others may use several layers of optical material to achieve their design performance.

<span class="mw-page-title-main">Digital holographic microscopy</span>

Digital holographic microscopy (DHM) is digital holography applied to microscopy. Digital holographic microscopy distinguishes itself from other microscopy methods by not recording the projected image of the object. Instead, the light wave front information originating from the object is digitally recorded as a hologram, from which a computer calculates the object image by using a numerical reconstruction algorithm. The image forming lens in traditional microscopy is thus replaced by a computer algorithm. Other closely related microscopy methods to digital holographic microscopy are interferometric microscopy, optical coherence tomography and diffraction phase microscopy. Common to all methods is the use of a reference wave front to obtain amplitude (intensity) and phase information. The information is recorded on a digital image sensor or by a photodetector from which an image of the object is created (reconstructed) by a computer. In traditional microscopy, which do not use a reference wave front, only intensity information is recorded and essential information about the object is lost.

Demetri Psaltis is a Greek-American electrical engineer who was the Dean of the School of Engineering at École Polytechnique Fédérale de Lausanne from 2007 to 2017. He is a professor in Bioengineering and Director of the Optics Laboratory of the EPFL. He is one of the founders of the term and the field of optofluidics. He is also well known for his past work in holography, especially with regards to optical computing, holographic data storage, and neural networks. He is an author of over 1100 publications, contributed more than 20 book chapters, invented more than 50 patents, and currently has a h-index of 98.

<span class="mw-page-title-main">Coherence scanning interferometry</span>

Coherence scanning interferometry (CSI) is any of a class of optical surface measurement methods wherein the localization of interference fringes during a scan of optical path length provides a means to determine surface characteristics such as topography, transparent film structure, and optical properties. CSI is currently the most common interference microscopy technique for areal surface topography measurement. The term "CSI" was adopted by the International Organization for Standardization (ISO).

Microstructured optical fibers (MOF) are optical fiber waveguides where guiding is obtained through manipulation of waveguide structure rather than its index of refraction.

Photonic molecules are a form of matter in which photons bind together to form "molecules". They were first predicted in 2007. Photonic molecules are formed when individual (massless) photons "interact with each other so strongly that they act as though they have mass". In an alternative definition, photons confined to two or more coupled optical cavities also reproduce the physics of interacting atomic energy levels, and have been termed as photonic molecules.

<span class="mw-page-title-main">Electromagnetic metasurface</span>

An electromagnetic metasurface refers to a kind of artificial sheet material with sub-wavelength thickness. Metasurfaces can be either structured or unstructured with subwavelength-scaled patterns in the horizontal dimensions.

An AI accelerator or neural processing unit is a class of specialized hardware accelerator or computer system designed to accelerate artificial intelligence and machine learning applications, including artificial neural networks and machine vision. Typical applications include algorithms for robotics, Internet of Things, and other data-intensive or sensor-driven tasks. They are often manycore designs and generally focus on low-precision arithmetic, novel dataflow architectures or in-memory computing capability. As of 2018, a typical AI integrated circuit chip contains billions of MOSFET transistors. A number of vendor-specific terms exist for devices in this category, and it is an emerging technology without a dominant design.

<span class="mw-page-title-main">Virtually imaged phased array</span> Dispersive optical device

A virtually imaged phased array (VIPA) is an angular dispersive device that, like a prism or a diffraction grating, splits light into its spectral components. The device works almost independently of polarization. In contrast to prisms or regular diffraction gratings, the VIPA has a much higher angular dispersion but has a smaller free spectral range. This aspect is similar to that of an Echelle grating, since it also uses high diffraction orders. To overcome this disadvantage, the VIPA can be combined with a diffraction grating. The VIPA is a compact spectral disperser with high wavelength resolving power.

Deep learning in photoacoustic imaging

Deep learning in photoacoustic imaging combines the hybrid imaging modality of photoacoustic imaging (PA) with the rapidly evolving field of deep learning. Photoacoustic imaging is based on the photoacoustic effect, in which optical absorption causes a rise in temperature, which causes a subsequent rise in pressure via thermo-elastic expansion. This pressure rise propagates through the tissue and is sensed via ultrasonic transducers. Due to the proportionality between the optical absorption, the rise in temperature, and the rise in pressure, the ultrasound pressure wave signal can be used to quantify the original optical energy deposition within the tissue.

A deep learning processor (DLP), or a deep learning accelerator, is an electronic circuit designed for deep learning algorithms, usually with separate data memory and dedicated instruction set architecture. Deep learning processors range from mobile devices, such as neural processing units (NPUs) in Apple iPhones or Huawei cellphones, and personal computers such as Apple silicon Macs, to cloud computing servers such as tensor processing units (TPU) in the Google Cloud Platform.

<span class="mw-page-title-main">Joseph Rosen (professor)</span> Israeli optoelectronics professor (born 1958)

Joseph Rosen is the Benjamin H. Swig Professor in Optoelectronics at the School of Electrical & Computer Engineering of Ben-Gurion University of the Negev, Israel.

References

  1. ↑ Qian, Chao; Lin, Xiao; Lin, Xiaobin; Xu, Jian; Sun, Yang; Li, Erping; Zhang, Baile; Chen, Hongsheng (2020). "Performing optical logic operations by a diffractive neural network". Light: Science & Applications . 9 (59): 59. Bibcode:2020LSA.....9...59Q. doi:10.1038/s41377-020-0303-2. PMC   7154031 . PMID   32337023.
  2. ↑ Wagner K, Psaltis D (1988). "Adaptive optical networks using photorefractive crystals". Appl. Opt. 27 (9): 1752–1759. Bibcode:1988ApOpt..27.1752P. doi:10.1364/AO.27.001752. PMID   20531647.
  3. ↑ Weverka R, Wagner K, Saffman M (1991). "Fully interconnected, two-dimensional neural arrays using wavelength-multiplexed volume holograms". Optics Letters. 16 (11): 826–828. Bibcode:1991OptL...16..826W. doi:10.1364/OL.16.000826. PMID   19776798.
  4. ↑ Wagner K, Psaltis D (1993). "Optical neural networks: an introduction by the feature editors". Appl. Opt. 32 (8): 1261–1263. Bibcode:1993ApOpt..32.1261W. doi:10.1364/AO.32.001261. PMID   20820259.
  5. ↑ Ramachandran R, Gunasekaran N (2000). "Optical Implementation of Two Dimensional Bipolar Hopfield Model Neural Network (Scientific Note)" (PDF). Proceedings-National Science Council Republic of China Part a Physical Science and Engineering. 24 (1): 73–8. Archived from the original (PDF) on 12 October 2004.
  6. ↑ Duvillier J, Killinger M, Heggarty K, Yao K, de Bougrenet de la Tocnaye JL (January 1994). "All-optical implementation of a self-organizing map: a preliminary approach". Applied Optics. 33 (2): 258–66. Bibcode:1994ApOpt..33..258D. doi:10.1364/AO.33.000258. PMID   20862015.
  7. ↑ Hejda M, Robertson J, Bueno J, Alanis J, Hurtado A (2021-06-01). "Neuromorphic encoding of image pixel data into rate-coded optical spike trains with a photonic VCSEL-neuron". APL Photonics. 6 (6): 060802. Bibcode:2021APLP....6f0802H. doi: 10.1063/5.0048674 . ISSN   2378-0967.
  8. ↑ Robertson J, Hejda M, Bueno J, Hurtado A (April 2020). "Ultrafast optical integration and pattern classification for neuromorphic photonics based on spiking VCSEL neurons". Scientific Reports. 10 (1): 6098. Bibcode:2020NatSR..10.6098R. doi:10.1038/s41598-020-62945-5. PMC   7142074 . PMID   32269249.
  9. ↑ George JK, Mehrabian A, Amin R, Meng J, de Lima TF, Tait AN, et al. (February 2019). "Neuromorphic photonics with electro-absorption modulators". Optics Express. 27 (4): 5181–5191. arXiv: 1809.03545 . Bibcode:2019OExpr..27.5181G. doi:10.1364/OE.27.005181. PMID   30876120. S2CID   80625696.
  10. ↑ Shainline JM (January 2020). "Fluxonic Processing of Photonic Synapse Events". IEEE Journal of Selected Topics in Quantum Electronics. 26 (1): 1–15. arXiv: 1904.02807 . Bibcode:2020IJSTQ..2627473S. doi:10.1109/JSTQE.2019.2927473. ISSN   1077-260X. S2CID   102352120.
  11. ↑ Romeira B, Javaloyes J, Ironside CN, Figueiredo JM, Balle S, Piro O (September 2013). "Excitability and optical pulse generation in semiconductor lasers driven by resonant tunneling diode photo-detectors". Optics Express. 21 (18): 20931–40. Bibcode:2013OExpr..2120931R. doi:10.1364/OE.21.020931. hdl: 10400.1/11954 . PMID   24103966. S2CID   480070.
  12. ↑ Lin, Xing; Rivenson, Yair; Yardimci, Nezih T.; Veli, Muhammed; Luo, Yi; Jarrahi, Mona; Ozcan, Aydogan (7 September 2018). "All-optical machine learning using diffractive deep neural networks". Science. 361 (6406): 1004–1008. arXiv: 1804.08711 . Bibcode:2018Sci...361.1004L. doi:10.1126/science.aat8084. PMID   30049787. S2CID   13753997.
  13. ↑ Chang, Julie; Sitzmann, Vincent; Dun, Xiong; Heidrich, Wolfgang; Wetzstein, Gordon (17 August 2018). "Hybrid optical-electronic convolutional neural networks with optimized diffractive optics for image classification". Scientific Reports. 8 (1): 12324. Bibcode:2018NatSR...812324C. doi:10.1038/s41598-018-30619-y. PMC   6098044 . PMID   30120316. S2CID   256961403.
  14. ↑ Li, Shurui; Miscuglio, Mario; Sorger, Volker J.; Gupta, Puneet (2020). "Channel Tiling for Improved Performance and Accuracy of Optical Neural Network Accelerators". arXiv: 2011.07391 [cs.ET].
This page is based on this Wikipedia article
Text is available under the CC BY-SA 4.0 license; additional terms may apply.
Images, videos and audio are available under their respective licenses.