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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 ]
The emergence of both deep learning neural networks based on phase modulation, [15] and more recently amplitude modulation using photonic memories [16] have created a new area of photonic technologies for neuromorphic computing, [17] [18] leading to new photonic computing technologies, all on a chip such as the photonic tensor core. [19]
This was later extended "Deep Cytometry" [15] in which the computationally intensive tasks of image processing and feature extraction before deep learning were avoided by directly feeding the time-stretch line scans, each representing a laser pulse into a deep convolutional neural network. This direct classification of raw time-stretched data ...
An AI accelerator, deep learning processor or neural processing unit (NPU) is a class of specialized hardware accelerator [1] or computer system [2] [3] designed to accelerate artificial intelligence (AI) and machine learning applications, including artificial neural networks and computer vision.
Deep learning is a subset of machine learning that focuses on utilizing neural networks to perform tasks such as classification, regression, and representation learning.The field takes inspiration from biological neuroscience and is centered around stacking artificial neurons into layers and "training" them to process data.
Physics-informed neural networks for solving Navier–Stokes equations. Physics-informed neural networks (PINNs), [1] also referred to as Theory-Trained Neural Networks (TTNs), [2] are a type of universal function approximators that can embed the knowledge of any physical laws that govern a given data-set in the learning process, and can be described by partial differential equations (PDEs).
), for 'analog deep learning'. [29] [30] Intel unveiled its neuromorphic research chip, called "Loihi", in October 2017. The chip uses an asynchronous spiking neural network (SNN) to implement adaptive self-modifying event-driven fine-grained parallel computations used to implement learning and inference with high efficiency. [31] [32]
The first application of deep learning in PACT was by Reiter et al. [8] in which a deep neural network was trained to learn spatial impulse responses and locate photoacoustic point sources. The resulting mean axial and lateral point location errors on 2,412 of their randomly selected test images were 0.28 mm and 0.37 mm respectively.