Convolutional Neural Networks (CNNs) are employed in a plethora of fields, including computer vision, natural language processing, and speech recognition. We present an integrated photonic accelerator for CNNs based on the temporal-spatial interleaving of signals. This architecture supports 1D kernels, and can be extended to 2D convolutional kernels, providing scalability for complex networks. A supervised on-chip learning algorithm is employed to guarantee a reliable setting of convolutional weights against fabrication tolerances, thermal cross-talks, and changes in operating conditions. Overall, by leveraging photonics technology, the proposed accelerator significantly reduces hardware complexity while enabling high-speed processing and parallelism.
KEYWORDS: Tunable filters, Optical filters, Neurons, Frequency combs, Multiplexing, Integrated optics, Linear filtering, Photonics, Signal processing, Signal attenuation
Reservoir Computers (RCs) are brain-inspired algorithms based on recurrent neural networks where only output weights are tuned, while internal weights remain untrained. We recently demonstrated a photonic frequency-multiplexing RC encoding neurons in the lines of a frequency comb. We also demonstrated a single-layer feed-forward neural network based on a similar frequency-multiplexing principle. Here we present the design for an integrated optical output layer for such frequency multiplexing based photonic neural networks. The all-optical output layer uses wavelength (de)multiplexers and wavelength converters to apply signed weights to neurons encoded in comb lines.
Reservoir Computers (RC) are brain-inspired algorithms that use partially untrained recurrent neural networks where only output connections are tuned. RCs can perform signal-analysis tasks such as distortion compensation. We recently demonstrated a photonic RC in which neurons are encoded in a frequency comb, untrained interconnections are realized by phase modulation, and trained output connections are realized by spectral filters. Here, we present a further development of this scheme in which the same substrate is used to implement two RCs simultaneously. The two RCs can either be used in parallel on different tasks, or in series, thereby implementing a “deep” RC.
We propose an optical implementation of an Extreme Learning Machine (ELM) inspired by frequency-multiplexing techniques previously employed for Reservoir Computing. The input layer of the ELM is encoded in the lines of a frequency comb and the hidden layer is generated by making comb lines interfere. Multiplication by output weights can be performed optically. This approach combines the potential high-speed, low-power and paral- lelization advantages of Optical Neural Networks with the cheap training (both in terms of speed and power) of ELMs, which do not require slow gradient descent and error backpropagation algorithms. We present preliminary experimental results compared with simulations.
We present novel methods to perform plenoptic imaging at the diffraction limit by measuring intensity correlations of light. The first method is oriented towards plenoptic microscopy, a promising technique which allows refocusing and depth-of-field enhancement, in post-processing, as well as scanning free 3D imaging. To overcome the limitations of standard plenoptic microscopes, we propose an adaptation of Correlation Plenoptic Imaging (CPI) to the working conditions of microscopy. We consider and compare different architectures of CPI microscopes, and discuss the improved robustness with respect to previous protocols against turbulence around the sample. The second method is based on measuring correlations between the images of two reference planes, arbitrarily chosen within the tridimensional scene of interest, providing an unprecedented combination of image resolution and depth of field. The results lead the way towards the realization of compact designs for CPI devices.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.