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Analysis and Design of an All-Optical Multi-Wavelength-Channel Neuron Using χ^(2)Nonlinearities in Neural Networks

Dehghani, Mohammad Mehdi | 2024

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 57262 (05)
  4. University: Sharif University of Technology
  5. Department: Electrical Engineering
  6. Advisor(s): Mehrany, Khashayar; Memarian, Mohammad
  7. Abstract:
  8. Optical neural networks (ONNs) are custom optical circuits promising a breakthrough in low-power, parallelized, and high-speed hardware for the growing demands of artificial intelligence applications. All-optical implementation of ONNs has proven burdensome chiefly due to the lack of optical devices that can emulate the neurons' non-linear activation function, thus forcing hybrid optical-electronic implementations. Moreover, ONNs suffer from a large footprint in comparison to their electronic (CMOS-based) counterparts. Utilizing virtual optical neurons in the time or frequency domain can reduce the number of required physical neurons, but an all-optical activation function is still required, especially where several layers comprised of multiple neurons are required for deep networks. In this thesis, we propose an all-optical multi-wavelength-channel rectified linear unit (ReLU) activation function, by leveraging χ^((2)) nonlinearity across more than 100 wavelength channels simultaneously. Our design significantly reduces the footprint of ONNs by consolidating all of the nonlinear activation functions present in each layer of an ONN into a single physical device with a broad bandwidth. This enables the realization of all-optical low-footprint ONNs with multiple layers made of several virtual neurons whose outputs are computed by a single ReLU activation function. Finally, we examine this all-optical activation function within the context of real and complex neural networks
  9. Keywords:
  10. Optical Artificial Neural Networks ; Optical Neural Networks ; Coherent Light ; Nonlinear Activation Functions ; Virtual Neuron

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