Silicon Photonic Architecture for Training Deep Neural Networks with Direct Feedback Alignment

no code implementations • 12 Nov 2021 • Matthew J. Filipovich, Zhimu Guo, Mohammed Al-Qadasi, Bicky A. Marquez, Hugh D. Morison, Volker J. Sorger, Paul R. Prucnal, Sudip Shekhar, Bhavin J. Shastri

There has been growing interest in using photonic processors for performing neural network inference operations; however, these networks are currently trained using standard digital electronics.

Takens-inspired neuromorphic processor: a downsizing tool for random recurrent neural networks via feature extraction

no code implementations • 6 Jul 2019 • Bicky A. Marquez, Jose Suarez-Vargas, Bhavin J. Shastri

We describe a new technique which minimizes the amount of neurons in the hidden layer of a random recurrent neural network (rRNN) for time series prediction.

Time Series Time Series Prediction

Digital Electronics and Analog Photonics for Convolutional Neural Networks (DEAP-CNNs)

1 code implementation • 23 Apr 2019 • Viraj Bangari, Bicky A. Marquez, Heidi B. Miller, Alexander N. Tait, Mitchell A. Nahmias, Thomas Ferreira de Lima, Hsuan-Tung Peng, Paul R. Prucnal, Bhavin J. Shastri

Convolutional Neural Networks (CNNs) are powerful and highly ubiquitous tools for extracting features from large datasets for applications such as computer vision and natural language processing.